experimental data theoretical

Before you go, check this out!

We have lots more on the site to show you. You've only seen one page. Check out this post which is one of the most popular of all time.

Theoretical vs. Experimental Probability: How do they differ?

Probability is the study of chances and is an important topic in mathematics. There are two types of probability: theoretical and experimental.

So, how to define theoretical and experimental probability? Theoretical probability is calculated using mathematical formulas, while experimental probability is based on results from experiments or surveys. In order words, theoretical probability represents how likely an event is to happen. On the other hand, experimental probability illustrates how frequently an event occurs in an experiment.

Read on to find out the differences between theoretical and experimental probability. If you wonder How to Understand Statistics Easily , I wrote a whole article where I share 9 helpful tips to help you Ace statistics.

Table of Contents

What Is Theoretical Probability?

Theoretical probability is calculated using mathematical formulas. In other words, a theoretical probability is a probability that is determined based on reasoning. It does not require any experiments to be conducted. Theoretical probability can be used to calculate the likelihood of an event occurring before it happens.

Keep in mind that theoretical probability doesn’t involve any experiments or surveys; instead, it relies on known information to calculate the chances of something happening.

For example, if you wanted to calculate the probability of flipping a coin and getting tails, you would use the formula for theoretical probability. You know that there are two possible outcomes—heads or tails—and that each outcome is equally likely, so you would calculate the probability as follows: 1/2, or 50%.

How Do You Calculate Theoretical Probability?

• First, start by counting the number of possible outcomes of the event.
• Second, count the number of desirable (favorable) outcomes of the event.
• Third, divide the number of desirable (favorable) outcomes by the number of possible outcomes.
• Finally, express this probability as a decimal or percentage.

The theoretical probability formula is defined as follows: Theoretical Probability = Number of favorable (desirable) outcomes divided by the Number of possible outcomes.

How Is Theoretical Probability Used in Real Life?

Probability plays a vital role in the day to day life. Here is how theoretical probability is used in real life: 

• Sports and gaming strategies
• Analyzing political strategies.
• Buying or selling insurance
• Determining blood groups 
• Online shopping
• Weather forecast
• Online games

What Is Experimental Probability?

Experimental probability, on the other hand, is based on results from experiments or surveys. It is the ratio of the number of successful trials divided by the total number of trials conducted. Experimental probability can be used to calculate the likelihood of an event occurring after it happens.

For example, if you flipped a coin 20 times and got heads eight times, the experimental probability of obtaining heads would be 8/20, which is the same as 2/5, 0.4, or 40%.

How Do You Calculate Experimental Probability?

The formula for the experimental probability is as follows:  Probability of an Event P(E) = Number of times an event happens divided by the Total Number of trials .

If you are interested in learning how to calculate experimental probability, I encourage you to watch the video below.

How Is Experimental Probability Used in Real Life?

Knowing experimental probability in real life provides powerful insights into probability’s nature. Here are a few examples of how experimental probability is used in real life:

• Rolling dice
• Selecting playing cards from a deck
• Drawing marbles from a hat
• Tossing coins

The main difference between theoretical and experimental probability is that theoretical probability expresses how likely an event is to occur, while experimental probability characterizes how frequently an event occurs in an experiment.

In general, the theoretical probability is more reliable than experimental because it doesn’t rely on a limited sample size; however, experimental probability can still give you a good idea of the chances of something happening.

The reason is that the theoretical probability of an event will invariably be the same, whereas the experimental probability is typically affected by chance; therefore, it can be different for different experiments.

Also, generally, the more trials you carry out, the more times you flip a coin, and the closer the experimental probability is likely to be to its theoretical probability.

Also, note that theoretical probability is calculated using mathematical formulas, while experimental probability is found by conducting experiments.

What to read next:

• Types of Statistics in Mathematics And Their Applications .
• Is Statistics Harder Than Algebra? (Let’s find out!)
• Should You Take Statistics or Calculus in High School?
• Is Statistics Hard in High School? (Yes, here’s why!)

Wrapping Up

Theoretical and experimental probabilities are two ways of calculating the likelihood of an event occurring. Theoretical probability uses mathematical formulas, while experimental probability uses data from experiments. Both types of probability are useful in different situations.

I believe that both theoretical and experimental probabilities are important in mathematics. Theoretical probability uses mathematical formulas to calculate chances, while experimental probability relies on results from experiments or surveys.

I am Altiné. I am the guy behind mathodics.com. When I am not teaching math, you can find me reading, running, biking, or doing anything that allows me to enjoy nature's beauty. I hope you find what you are looking for while visiting mathodics.com.

Recent Posts

How to Find the Y-Value of Stationary Points with TI-84 Plus CE

TI-84 Plus CE Calculator If you’re studying calculus or any advanced math course, you will certainly come across the concept of stationary points. So, what is a stationary point? A...

IB Maths Vs. A-Level Maths - Which One is Harder?

Maths is a subject that can be demanding for many students. It not only requires strong analytical skills but also an ability to handle complex concepts with ease. Students looking to further their...

Browse Course Material

Course info, instructors.

• Prof. Eric Grimson
• Prof. John Guttag
• Dr. Ana Bell

Departments

• Electrical Engineering and Computer Science

As Taught In

• Computer Science
• Probability and Statistics

Learning Resource Types

Introduction to computational thinking and data science, lecture 9: understanding experimental data.

Description: Prof. Grimson talks about how to model experimental data in a way that gives a sense of the underlying mechanism and to predict behaviour in new settings.

Instructor: Eric Grimson

• Download video
• Download transcript

You are leaving MIT OpenCourseWare

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Perspective
• Published: 07 June 2021

Integration of theory and experiment in the modelling of heterogeneous electrocatalysis

• Sharon Hammes-Schiffer   ORCID: orcid.org/0000-0002-3782-6995 1 &
• Giulia Galli   ORCID: orcid.org/0000-0002-8001-5290 2 , 3  

Nature Energy volume  6 ,  pages 700–705 ( 2021 ) Cite this article

5330 Accesses

50 Citations

13 Altmetric

Metrics details

• Computational chemistry
• Electrocatalysis
• Hydrogen energy
• Solar fuels

Heterogeneous electrocatalysis is critical to many energy conversion processes. Theoretical and computational approaches are essential to interpret experimental data and provide the mechanistic understanding necessary to design more effective catalysts. However, automated general procedures to build predictive theoretical and computational frameworks are not readily available; specific choices must be made in terms of the atomistic structural model and the level of theory, as well as the experimental data used to inform and validate these choices. Here we outline some best practices for modelling heterogeneous systems and present examples in the context of catalysis at metal electrodes and oxides. The level of theory should be chosen for the specific system and properties of interest, and experimental validation is essential from the beginning to the end of the study. Continuous feedback and ultimate integration between experiment and theory enhances the power of calculations to elucidate mechanisms, identify effective descriptors and clarify design principles.

This is a preview of subscription content, access via your institution

Access options

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

24,99 € / 30 days

cancel any time

Subscribe to this journal

Receive 12 digital issues and online access to articles

111,21 € per year

only 9,27 € per issue

Buy this article

• Purchase on SpringerLink
• Instant access to full article PDF

Prices may be subject to local taxes which are calculated during checkout

Similar content being viewed by others

Enhancing the connection between computation and experiments in electrocatalysis

Bayesian data analysis reveals no preference for cardinal Tafel slopes in CO 2 reduction electrocatalysis

The concept of active site in heterogeneous catalysis

Greeley, J. Theoretical heterogeneous catalysis: scaling relationships and computational catalyst design. Annu. Rev. Chem. Biomol. 7 , 605–635 (2016).

Article   Google Scholar  

Bruix, A., Margraf, J. T., Andersen, M. & Reuter, K. First-principles-based multiscale modelling of heterogeneous catalysis. Nat. Catal. 2 , 659–670 (2019).

Quesne, M. G., Silveri, F., de Leeuw, N. H. & Catlow, C. R. A. Advances in sustainable catalysis: a computational perspective. Front. Chem. 7 , 182 (2019).

Schlexer Lamoureux, P. et al. Machine learning for computational heterogeneous catalysis. ChemCatChem 11 , 3581–3601 (2019).

Cheng, J. & Sprik, M. Alignment of electronic energy levels at electrochemical interfaces. Phys. Chem. Chem. Phys. 14 , 11245–11267 (2012).

Kharche, N., Muckerman, J. T. & Hybertsen, M. S. First-principles approach to calculating energy level alignment at aqueous semiconductor interfaces. Phys. Rev. Lett. 113 , 176802 (2014).

Pham, T. A., Ping, Y. & Galli, G. Modelling heterogeneous interfaces for solar water splitting. Nat. Mater. 16 , 401–408 (2017).

Rousseau, R., Glezakou, V.-A. & Selloni, A. Theoretical insights into the surface physics and chemistry of redox-active oxides. Nat. Rev. Mater. 5 , 460–475 (2020).

Selcuk, S. & Selloni, A. Facet-dependent trapping and dynamics of excess electrons at anatase TiO 2 surfaces and aqueous interfaces. Nat. Mater. 15 , 1107–1112 (2016).

Gerosa, M., Gygi, F., Govoni, M. & Galli, G. The role of defects and excess surface charges at finite temperature for optimizing oxide photoabsorbers. Nat. Mater. 17 , 1122–1127 (2018).

Janthon, P. et al. Bulk properties of transition metals: a challenge for the design of universal density functionals. J. Chem. Theory Comput. 10 , 3832–3839 (2014).

Mandal, S., Haule, K., Rabe, K. M. & Vanderbilt, D. Systematic beyond-DFT study of binary transition metal oxides. NPJ Comput. Mater. 5 , 115 (2019).

Lejaeghere, K. et al. Reproducibility in density functional theory calculations of solids. Science 351 , aad3000 (2016).

Gauthier, J. A. et al. Unified approach to implicit and explicit solvent simulations of electrochemical reaction energetics. J. Chem. Theory Comput. 15 , 6895–6906 (2019).

Verma, P. & Truhlar, D. G. Status and challenges of density functional theory. Trends Chem. 2 , 302–318 (2020).

Perdew, J. P., Burke, K. & Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett. 77 , 3865–3868 (1996).

Scherpelz, P., Govoni, M., Hamada, I. & Galli, G. Implementation and validation of fully relativistic GW calculations: spin–orbit coupling in molecules, nanocrystals, and solids. J. Chem. Theory Comput. 12 , 3523–3544 (2016).

Andreussi, O., Dabo, I. & Marzari, N. Revised self-consistent continuum solvation in electronic-structure calculations. J. Chem. Phys. 136 , 064102 (2012).

Hörmann, N. G., Andreussi, O. & Marzari, N. Grand canonical simulations of electrochemical interfaces in implicit solvation models. J. Chem. Phys. 150 , 041730 (2019).

Solis, B. H. & Hammes-Schiffer, S. Proton-coupled electron transfer in molecular electrocatalysis: theoretical methods and design principles. Inorg. Chem. 53 , 6427–6443 (2014).

Pham, T. A., Lee, D., Schwegler, E. & Galli, G. Interfacial effects on the band edges of functionalized Si surfaces in liquid water. J. Am. Chem. Soc. 136 , 17071–17077 (2014).

Venkataraman, C., Soudackov, A. V. & Hammes-Schiffer, S. Theoretical formulation of nonadiabatic electrochemical proton-coupled electron transfer at metal−solution interfaces. J. Phys. Chem. C 112 , 12386–12397 (2008).

Goldsmith, Z. K., Lam, Y. C., Soudackov, A. V. & Hammes-Schiffer, S. Proton discharge on a gold electrode from triethylammonium in acetonitrile: theoretical modeling of potential-dependent kinetic isotope effects. J. Am. Chem. Soc. 141 , 1084–1090 (2019).

Lam, Y.-C., Soudackov, A. V. & Hammes-Schiffer, S. Kinetics of proton discharge on metal electrodes: effects of vibrational nonadiabaticity and solvent dynamics. J. Phys. Chem. Lett. 10 , 5312–5317 (2019).

Lam, Y.-C., Soudackov, A. V. & Hammes-Schiffer, S. Theory of electrochemical proton-coupled electron transfer in diabatic vibronic representation: application to proton discharge on metal electrodes in alkaline solution. J. Phys. Chem. C 124 , 27309–27322 (2020).

Bard, A. J. & Faulkner, L. R. Electrochemical Methods: Fundamentals and Applications 2nd edn (John Wiley & Sons, 2001).

Ulman, K., Poli, E., Seriani, N., Piccinin, S. & Gebauer, R. Understanding the electrochemical double layer at the hematite/water interface: a first principles molecular dynamics study. J. Chem. Phys. 150 , 041707 (2018).

Jackson, M. N. & Surendranath, Y. Donor-dependent kinetics of interfacial proton-coupled electron transfer. J. Am. Chem. Soc. 138 , 3228–3234 (2016).

Sakaushi, K. Quantum proton tunneling in multi-electron/-proton transfer electrode processes. Faraday Discuss. 221 , 428–448 (2020).

Kastlunger, G., Lindgren, P. & Peterson, A. A. Controlled-potential simulation of elementary electrochemical reactions: proton discharge on metal surfaces. J. Phys. Chem. C 122 , 12771–12781 (2018).

Goldsmith, Z. K., Secor, M. & Hammes-Schiffer, S. Inhomogeneity of interfacial electric fields at vibrational probes on electrode surfaces. ACS Cent. Sci. 6 , 304–311 (2020).

Melander, M. M., Kuisma, M. J., Christensen, T. E. K. & Honkala, K. Grand-canonical approach to density functional theory of electrocatalytic systems: Thermodynamics of solid-liquid interfaces at constant ion and electrode potentials. J. Chem. Phys. 150 , 041706 (2018).

Schkolnik, G. et al. Vibrational Stark effect of the electric-field reporter 4-mercaptobenzonitrile as a tool for investigating electrostatics at electrode/SAM/solution interfaces. Int. J. Mol. Sci. 13 , 7466–7482 (2012).

Patrow, J. G., Sorenson, S. A. & Dawlaty, J. M. Direct spectroscopic measurement of interfacial electric fields near an electrode under polarizing or current-carrying conditions. J. Phys. Chem. C 121 , 11585–11592 (2017).

Sorenson, S. A., Patrow, J. G. & Dawlaty, J. M. Solvation reaction field at the interface measured by vibrational sum frequency generation spectroscopy. J. Am. Chem. Soc. 139 , 2369–2378 (2017).

Jackson, M. N. & Surendranath, Y. Molecular control of heterogeneous electrocatalysis through graphite conjugation. Acc. Chem. Res. 52 , 3432–3441 (2019).

Warburton, R. E. et al. Interfacial field-driven proton-coupled electron transfer at graphite-conjugated organic acids. J. Am. Chem. Soc. 142 , 20855–20864 (2020).

Lee, D. et al. The impact of surface composition on the interfacial energetics and photoelectrochemical properties of BiVO 4 . Nat. Energy 6 , 287–294 (2021).

Wiktor, J. & Pasquarello, A. Electron and hole polarons at the BiVO 4 –water interface. ACS Appl. Mater. Interfaces 11 , 18423–18426 (2019).

Wang, W. et al. The role of surface oxygen vacancies in BiVO 4 . Chem. Mater. 32 , 2899–2909 (2020).

Goldsmith, Z. K. et al. Characterization of NiFe oxyhydroxide electrocatalysts by integrated electronic structure calculations and spectroelectrochemistry. Proc. Natl Acad. Sci. USA 114 , 3050–3055 (2017).

Ping, Y., Goddard, W. A. & Galli, G. A. Energetics and solvation effects at the photoanode/catalyst interface: ohmic contact versus Schottky barrier. J. Am. Chem. Soc. 137 , 5264–5267 (2015).

Spurgeon, J. M., Velazquez, J. M. & McDowell, M. T. Improving O 2 production of WO 3 photoanodes with IrO 2 in acidic aqueous electrolyte. Phys. Chem. Chem. Phys. 16 , 3623–3631 (2014).

Di Valentin, C. & Selloni, A. Bulk and surface polarons in photoexcited anatase TiO 2 . J. Phys. Chem. Lett. 2 , 2223–2228 (2011).

Hoster, H. E., Alves, O. B. & Koper, M. T. M. Tuning adsorption via strain and vertical ligand effects. ChemPhysChem 11 , 1518–1524 (2010).

van der Niet, M. J. T. C., Garcia-Araez, N., Hernández, J., Feliu, J. M. & Koper, M. T. M. Water dissociation on well-defined platinum surfaces: the electrochemical perspective. Catal. Today 202 , 105–113 (2013).

Hansen, H. A., Viswanathan, V. & Nørskov, J. K. Unifying kinetic and thermodynamic analysis of 2 e – and 4 e – reduction of oxygen on metal surfaces. J. Phys. Chem. C 118 , 6706–6718 (2014).

Rossmeisl, J. et al. Realistic cyclic voltammograms from ab initio simulations in alkaline and acidic electrolytes. J. Phys. Chem. C 124 , 20055–20065 (2020).

Tiwari, A. et al. Fingerprint voltammograms of copper single crystals under alkaline conditions: a fundamental mechanistic analysis. J. Phys. Chem. Lett. 11 , 1450–1455 (2020).

Nong, H. N. et al. Key role of chemistry versus bias in electrocatalytic oxygen evolution. Nature 587 , 408–413 (2020).

Greeley, J., Jaramillo, T. F., Bonde, J., Chorkendorff, I. & Nørskov, J. K. Computational high-throughput screening of electrocatalytic materials for hydrogen evolution. Nat. Mater. 5 , 909–913 (2006).

Zhong, M. et al. Accelerated discovery of CO 2 electrocatalysts using active machine learning. Nature 581 , 178–183 (2020).

Govoni, M. et al. Qresp, a tool for curating, discovering and exploring reproducible scientific papers. Sci. Data 6 , 190002 (2019).

Blaiszik, B. et al. The materials data facility: data services to advance materials science research. JOM 68 , 2045–2052 (2016).

Download references

Acknowledgements

This material is based on work supported by the National Science Foundation grant no. CHE-1764399 (G.G.) and the Air Force Office of Scientific Research under awards FA9550-18-1-0420 and FA9550-18-1-0134 (S.H.-S.).

Author information

Authors and affiliations.

Department of Chemistry, Yale University, New Haven, CT, USA

Sharon Hammes-Schiffer

Pritzker School of Molecular Engineering and Department of Chemistry, University of Chicago, Chicago, IL, USA

Giulia Galli

Materials Science Division, Argonne National Laboratory, Lemont, IL, USA

You can also search for this author in PubMed   Google Scholar

Corresponding authors

Correspondence to Sharon Hammes-Schiffer or Giulia Galli .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Peer review information Nature Energy thanks Michal Bajdich, Annabella Selloni and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Cite this article.

Hammes-Schiffer, S., Galli, G. Integration of theory and experiment in the modelling of heterogeneous electrocatalysis. Nat Energy 6 , 700–705 (2021). https://doi.org/10.1038/s41560-021-00827-4

Download citation

Received : 02 November 2020

Accepted : 31 March 2021

Published : 07 June 2021

Issue Date : July 2021

DOI : https://doi.org/10.1038/s41560-021-00827-4

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Alphamat: a material informatics hub connecting data, features, models and applications.

• Zhilong Wang

npj Computational Materials (2023)

Advance in 3D self-supported amorphous nanomaterials for energy storage and conversion

• Baohong Zhang

Nano Research (2023)

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

• > Statistics
• > Probability

Mastering Theoretical and Experimental Probability Comparisons Dive into the world of probability with our comprehensive guide. Learn to calculate, compare, and apply theoretical and experimental probability in real-world scenarios. Enhance your statistical skills today!

Get the most by viewing this topic in your current grade. Pick your course now .

• Experimental probability VS. Theoretical probability

What is the experimental probability of both coins landing on heads?

• Calculate the theoretical probability of both coins landing on heads.
• Compare the theoretical probability and experimental probability.
• What can Jessie do to decrease the difference between the theoretical probability and experimental probability?

Free to Join!

Join for Free

Easily See Your Progress

Make Use of Our Learning Aids

Last Viewed

Practice accuracy, suggested tasks.

Get quick access to the topic you're currently learning.

See how well your practice sessions are going over time.

Stay on track with our daily recommendations.

Earn Achievements as You Learn

Create and Customize Your Avatar

What is experimental probability?

In math, when we deal with probability , we may be asked for the experimental probability of an experiment. What this means is that they're looking for the probability of something happening based off the results of an actual experiment. This is the experimental probability definition.

So for example, if you're asked for the probability of getting heads after flipping a coin 10 times, the experimental probability will be the number of times you got heads after flipping a coin 10 times. Let's say that you got 6 heads out of your 10 throws. Then your experimental probability is 6/10, or 60%.

For theoretical probability, it doesn't require you to actually do the experiment and then look at the results. Instead, the theoretical probability is what you expect to happen in an experiment (the expected probability). This is the theoretical probability definition.

In the case of the coin flips, since there's 2 sides to a coin and there's an equal chance that either side will land when you flip it, the theoretical probability should be 1 2 \frac{1}{2} 2 1 ​ or 50%.

Why is there a difference in theoretical and experimental probability? The relationship between the two is that you'll find if you do the experiment enough times, the experimental probability will get closer and closer to the theoretical probability's answer. You can try this out yourself with a coin. You likely won't get exactly 50% for both heads and tails from your first 10 throws, but as you throw a coin 50 times or even 100 times, you'll see the experimental probability's answer getting closer to 50%.

We'll now see how experimental and theoretical probability works with these questions.

Question 1a: Two coins are flipped 20 times to determine the experimental probability of landing on heads versus tails. The results are in the chart below:

We are looking for the experimental probability of both coins landing on heads. Looking at the table in the question, we know that there were 4 out of 20 trials in which both coins landed on heads. So the experimental probability is 4 20 \frac{4}{20} 20 4 ​ , which equals to 1 5 \frac{1}{5} 5 1 ​ (20%) after simplifying the fraction

Question 1b: Calculate the theoretical probability of both coins landing on heads.

Now, we are looking for the theoretical probability. First, there are 4 possible outcomes (H,H), (H, T), (T,H), (T, T). 1 out of the 4 possible outcomes has both coins land on heads. So, the theoretical probability is 1 4 \frac{1}{4} 4 1 ​ or 25%

Question 1c: Compare the theoretical probability and experimental probability.

From the previous parts, we know that the experimental probability of both coins landing on head equals 20%, while in theory, there should be a 25% chance that both coins lands on head. Therefore, the theoretical probability is higher than the experimental probability.

Question 1d:

What can we do to reduce the difference between the experimental probability and theoretical probability? We can simply continue the experimental by flipping the coin for many more times —say, 20,000 times. When more trials are performed, the difference between experimental probability and theoretical probability will diminish. The experimental probability will gradually get closer to the value of the theoretical probability. In this case, the experimental probability will get closer to 25% as the coins is tossed over more times.

If you're looking for more experimental vs.theoretical probability examples, feel free to try out this question . It'll require you to do some hands-on experimentation!

• Introduction to probability
• Organizing outcomes
• Probability of independent events
• Determining probabilities using tree diagrams and tables
• Probability

Become a member to get more!

No Javascript

It looks like you have javascript disabled.

You can still navigate around the site and check out our free content, but some functionality, such as sign up, will not work.

If you do have javascript enabled there may have been a loading error; try refreshing your browser.

• Kindergarten
• Middle School
• High School
• Math Worksheets
• Language Arts
• Social Studies

Theoretical vs. Experimental Probability

More Topics

• Handwriting
• Difference Between
• 2020 Calendar
• Online Calculators
• Multiplication

Educational Videos

• Coloring Pages
• Privacy policy
• Terms of Use

© 2005-2020 Softschools.com

Experimental Data for Model Validation

Cite this chapter.

• David J. Murray-Smith 9  

Part of the book series: Simulation Foundations, Methods and Applications ((SFMA))

2224 Accesses

This chapter is concerned with experimental modelling techniques and the use of experimental test data for the purposes of model evaluation. It includes an overview of system identification and parameter estimation methods and gives emphasis to the importance of issues associated with the optimisation of the model structure. In the context of model validation, the potential “generalisation” capabilities of an identified model are considered in terms of model predictions for experimental situations which are not exactly the same as those used in the identification process. Issues of over-fitting and under-fitting are also discussed and procedures for the estimation of parameters of physically-based nonlinear models are considered, including evolutionary computing approaches, such as Genetic Algorithms and Genetic Programming. Issues of identifiability are discussed in some detail, as are questions of experimental design, the selection of the most appropriate test-input signals for system identification and model validation, together with ways of assessing the accuracy of parameter estimates.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime
• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Murray-Smith DJ (2012) Modelling and simulation of integrated systems in engineering: issues of methodology, quality, testing and application. Woodhead Publishing, Cambridge

Book   Google Scholar  

Oberkampf WL (2007) Predictive capabilities in computational science and engineering. Presented at OASCR applied mathematics PI meeting, Lawrence Livermore National Laboratory, 22–24 May 2007. http://science.energy.gov/~/media/ascr/pdf/workshops-conferences/mathtalks/Oberkampf.pdf . Accessed 9 June 2015

Dyson F (2004) A meeting with Enrico Fermi. Nature 427:297

Article   Google Scholar  

Mayer J, Khairy K, Howard J (2010) Drawing an elephant with four complex parameters. Am J Phys 78:648–649

Tomović R (1963) Sensitivity analysis of dynamic systems. McGraw-Hill, New York

Google Scholar  

Frank PM (1978) An introduction to system sensitivity theory. Academic, London

Ljung L (1999) System identification: theory for the user, 2nd edn. Prentice Hall, Upper Saddle River

Söderström T, Stoica P (1989) System identification. Prentice-Hall, New York

MATH   Google Scholar  

Nelles O (2001) Nonlinear system identification. Springer, Berlin

Book   MATH   Google Scholar  

Raol JR, Girija G, Singh J (2004) Modelling and parameter estimation of dynamic systems, IET control engineering series no. 65. IET, London

Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison Wesley, Boston, MA

Bar M, Hegger R, Kantz H (1999) Fitting differential equations to space-time dynamics. Phys Rev E 59:337–342

Müller TG, Timmer J (2002) Fitting parameters in partial differential equations from partially observed noisy data. Phys D 171:1–7

Article   MathSciNet   MATH   Google Scholar  

Xun X, Cao J, Mallick B et al (2013) Parameter estimation of partial differential equation models. J Am Stat Assoc 108(503). doi: 10.1080/01621459.2013.794730

Ewins DJ (2000) Basics and state-of-the-art of modal testing. Sãdhanã 25(3):207–220

Grafe H (1998) Model updating of large structural dynamics models using measured response functions. PhD dissertation, Imperial College, London

Kershen G (2002) On the model validation in non-linear structural dynamics, doctoral dissertation, University of Liege, Belgium

Simscape™ software, Mathworks Inc., [Online] http://www.mathworks.com/products/ . Accessed 5 June 2015

Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge, MA

Matlab®/Simulink® modelling and simulation software, Mathworks Inc. http://www.mathworks.com/products/ . Accessed 5 June 2015

Gray GJ, Li Y, Murray-Smith DJ et al (1996) Structural system identification using genetic programming and a block diagram oriented simulation tool. Electron Lett 32(16):1422–1424

Bellman R, Astrom KJ (1970) On structural identifiability. Math Biosci 7:329–339

Grewal MS, Glover K (1976) Identifiability of linear and nonlinear dynamical systems. IEEE Trans Autom Control 21:833–837

Milanese M, Molino GP (1975) Structural identifiability of compartmental models and pathophysiological information from the kinetics of drugs. Math Biosci 26:175–190

Article   MATH   Google Scholar  

Brown F, Godfrey KR (1978) Problems of determinacy in compartmental modeling with application to bilirubin kinetics. Math Biosci 40:205–224

Beck JV, Arnold KJ (1977) Parameter estimation in science and engineering. Wiley, New York

Cobelli C, Romanin-Jacur G (1976) Controllability, observability and structural identifiability of multi input multi output biological compartmental systems. IEEE Trans Biomed Eng 23(2):93–100

Hunter WG, Hill WJ, Henson TL (1969) Designing experiments for precise estimation of all or some of the constants in a mechanistic model. Can J Chem Eng 47:76–80

Titterington M (1980) Aspects of optimal design in dynamic systems. Technometrics 22(3):287–299

van den Bos A (1993) Periodic test signals – properties and use. In: Godfrey KR (ed) Perturbation signals for system identification. Prentice-Hall, Hemel Hempstead, pp 161–175

Godfrey KR (ed) (1993) Perturbation signals for system identification. Prentice-Hall, Hemel Hempstead, pp 60–125

Schoukens J, Guillaume P, Pintelon R (1993) Design of broadband excitation signals. In: Godfrey KR (ed) Perturbation signals for system identification. Prentice-Hall, Hemel Hempstead, pp 126–160

Harris SL (1993) Generation and application of binary multi-frequency signals. In: Godfrey KR (ed) Perturbation signals for system identification. Prentice-Hall, Hemel Hempstead, pp 209–223

Maine R, Iliffe R (1985) Identification of dynamic systems – theory and implementation. NASA report RP-1138, NASA, Dryden Flight Research Center, Edwards, CA

Tischler MB, Remple RK (2006) Aircraft and rotorcraft system identification. AIAA, Reston, VA

Download references

Author information

Authors and affiliations.

School of Engineering, University of Glasgow, Glasgow, UK

David J. Murray-Smith

You can also search for this author in PubMed   Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Murray-Smith, D.J. (2015). Experimental Data for Model Validation. In: Testing and Validation of Computer Simulation Models. Simulation Foundations, Methods and Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-15099-4_5

Download citation

DOI : https://doi.org/10.1007/978-3-319-15099-4_5

Publisher Name : Springer, Cham

Print ISBN : 978-3-319-15098-7

Online ISBN : 978-3-319-15099-4

eBook Packages : Computer Science Computer Science (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Stack Exchange Network

Stack Exchange network consists of 183 Q&A communities including Stack Overflow , the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.

Q&A for work

Connect and share knowledge within a single location that is structured and easy to search.

How to calculate theoretical and experimental data in general physics´experiments?

Im struggling how to calculate theoretical and experimental data with the added formulas and second Newton law. I did a free body diagram but it doesnt clarify how to calculate it. Any suggestion will be welcome. thank you.

The experimental data is just the measurement of the time to reach each sensor, so the two lines above the chart. You should copy the times into the second row of the chart. From the inclination of the plane you should be able to calculate a predicted acceleration due to gravity. Presumably you start with the ball at rest, so $v_0=0$ . You can then predict the velocity as a function of time from your equation, the time the ball should have passed each sensor, and compare that with the measured data. I am not sure how your professor expects you to come up with the experimental values of velocity and acceleration. It might be an overall fit to get the acceleration. It might be computing the change in distance divided by the change in time, but that has the problem that the velocity is constantly changing.

• $\begingroup$ Thanks. So I think that my teacher wants to complete the chart with the added formulas. I can do it but, I need to clarify what is theoretical and what experimental. It seems confusing at first sight. $\endgroup$ –  Charlie Van Basten Øydne Commented Aug 13, 2020 at 1:35

You must log in to answer this question.

Not the answer you're looking for browse other questions tagged physics ..

• Featured on Meta
• User activation: Learnings and opportunities
• Preventing unauthorized automated access to the network

Hot Network Questions

• Adjective separated from it's noun
• Why do the eigenvalues of the sum of matrices add up in my example?
• How to export SVG without anti-aliasing?
• Do "always prepared" Spells cost spell slots?
• Is creating my own gods better than using existing ones?
• Figure out which of your friends reveals confidential information to the media!
• How can one be compensated for loss caused negligently by someone who dies in the process?
• How can I avoid gaps at the intersection of two lines in TikZ? Or is there a better way to draw them?
• Why doesn't SpaceX use a normal blast trench like Saturn V?
• "immer noch" meaning "still"
• If I was ever deported, would it only be to my country of citizenship? Or can I arrange something else?
• Is "spell me" not US idiom? Or perhaps archaic now?
• How can an RS-422 transceiver not have a negative voltage rail?
• Is p→p a theorem in intuitionistic logic?
• Reliability of the hadith, The Friday prayer in congregation is a necessary duty for every Muslim, with four exceptions; a slave, a woman
• How can the doctor measure out a dose (dissolved in water) of exactly 10% of a tablet?
• Execute 'Iterate Feature Selection' in ModelBuilder only if Features are Selected
• What made scientists think that chemistry is reducible to physics and when did that happen?
• Disclaimer of Warranty regarding wording of representation, undertaking, warranties and gurantees
• Geometry Node group to efficiently find a common edge between two faces
• What determines the resistance with which MOSFETs fail short?
• is it possible that cohesive energy of a material is positive but the phonon dispersion curve have negative frequencies?
• What is the point of "what is the point?" argument in re determinism?
• Looking for a book where a boy is transported to another world by a beam of light

NTRS - NASA Technical Reports Server

Available downloads, related records.

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Journal Proposal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Estimating treatment effects using observational data and experimental data with non-overlapping support.

1. Introduction

• Regress Y P on Y S and X to obtain an estimate of μ , denoted as μ ^ .
• Estimate the conditional average treatment effect τ ( x ) on the surrogate outcome Y S , obtaining an estimate τ ^ .
• Define Y ^ i S ( 1 ) = Y i S if W i = 1 and Y ^ i S ( 1 ) = Y i S + τ ^ ( X i ) if W i = 0 .
• Estimate E Y i P ( 1 ) by 1 N O ∑ i = 1 N O μ ^ ( X i , Y ^ i S ( 1 ) ) .
• Define Y ^ i S ( 0 ) = Y i S if W i = 0 and Y ^ i S ( 0 ) = Y i S − τ ^ ( X i ) if W i = 1 .
• Estimate E Y i P ( 0 ) by 1 N O ∑ i = 1 N O μ ^ ( X i , Y ^ i S ( 0 ) ) .
• The final estimate is τ ^ P = 1 N O ∑ i = 1 N O μ ^ ( X i , Y ^ i S ( 1 ) ) − 1 N O ∑ i = 1 N O μ ^ ( X i , Y ^ i S ( 0 ) ) .

4. Applications

4.1. covariate support mismatch between samples.

• Apply any CATE estimation algorithm on the observational sample to obtain an estimate ω ^ .
• Solve the following optimization problem to obtain θ ^ : θ ^ = arg min θ ∑ i = 1 N E q E ( X i ) Y i − ω ^ ( X i ) − θ T X i 2 .
• Define τ ^ ( x ) = θ ^ T x + ω ^ ( x ) .

4.2. Instrumental Variable (IV) Setting in the Experimental Sample

4.2.1. constant effect, 4.2.2. non-parametric iv.

• Flexibility: GRF is capable of modeling complex, non-linear relationships between the covariates and the treatment effect, which is often essential in practical applications where such relationships are not adequately captured by parametric models.
• Generalizability: One notable advantage of GRF is its ability to generalize beyond binary treatment variables. As discussed in Athey et al. ( 2019 ), GRF can be extended to settings where the treatment variable W is a real-valued continuous variable.

4.3. Instrumental Variable Setting with Different Support of Pre-Treatment Covariates

• Apply any conditional average treatment effect (CATE) estimation algorithm, denoted by Q , to the set { X i , W i , Y i S } i = 1 m to obtain an initial estimate ω ^ ( x ) .
• Solve the following optimization problem on the experimental sample to refine the estimate: θ ^ = arg min θ ∑ i = 1 n m ^ ( x i ) − μ ^ ( x i ) π ^ ( x i ) − θ T x i + ω ^ ( x i ) γ ^ ( x i ) − e ^ ( x i ) π ^ ( x i ) 2 .
• Use the combined estimate ω ^ ( x ) + θ ^ T x as the final estimate of the CATE on the surrogate.

5. Simulations

• Presence of Confounding: We vary ω to be either 0 or 1. If ω = 0 , there is no confounding; otherwise, there is confounding, and we are in the non-parametric instrumental variable (IV) model.
• Sparsity of the Signal: We set κ τ to be either 2 or 4 to vary the sparsity of the signal.
• Additivity of the Signal: When true, τ ( x ) = ∑ j = 1 κ τ max { 0 , x j } ; when false, τ ( x ) = max { 0 , ∑ j = 1 κ τ x j } .
• Presence of Nuisance Terms: When true, μ ( x ) = 3 max { 0 , x 5 } + 3 max { 0 , x 6 } or μ ( x ) = 3 max { 0 , x 5 + x 6 } depending on the additive signal condition; when false, μ ( x ) = 0 .
• Identical Support: When true, we assume the distribution of the covariates in the experimental sample and that in the observational sample are the same; when false, X i ∼ N ( [ 1 , ⋯ , 1 ] T , I p × p ) in the observational sample.

6. A Real Data Example

• Use D g t to calculate the average treatment effect of average grade in year 3. This estimate τ g t will be viewed as the ground truth.
• Repeat the following steps 500 times.
• Sample n exp rural or inner-city students together with all the covariates except the student location covariate, treatment variable and average grade in year 1. This is our experimental sample D E .
• Sample n obs / 4 rural or inner-city students in control group that are not sampled in experimental sample, sample n obs / 4 rural or inner-city students in treatment group whose year 1 average grade is in the lower half among treated rural or inner-city students, sample n obs / 4 urban or suburban students in control group, and finally sample n obs / 4 urban or suburban students in treatment group whose year 1 average grade is in the lower half among treated urban or suburban students. This is our observational sample (which is confounded because we remove students with higher scores selectively from the population) D O .
• Use different methods to estimate τ g t based on D E and D O .
• Compare based on mean squared error (MSE).

7. Conclusions

Author contributions, data availability statement, conflicts of interest.

• Achilles, Charles M., Helen Pate Bain, Fred Bellott, Jayne Boyd-Zaharias, Jeremy Finn, John Folger, John Johnston, and Elizabeth Word. 2008. Tennessee’s Student Teacher Achievement Ratio (STAR) Project. Available online: https://doi.org/10.7910/DVN/SIWH9F (accessed on 1 September 2021).
• Angrist, Joshua D., and Jörn-Steffen Pischke. 2009. Mostly Harmless Econometrics: An Empiricist’s Companion . Number 8769 in Economics Books. Princeton: Princeton University Press. [ Google Scholar ]
• Athey, Susan, Guido Imbens, Jonas Metzger, and Evan Munro. 2020. Using wasserstein generative adversarial networks for the design of monte carlo simulations. Journal of Econometrics 240: 105076. [ Google Scholar ] [ CrossRef ]
• Athey, Susan, Julie Tibshirani, and Stefan Wager. 2019. Generalized random forests. Annals of Statistics 47: 1148–78. [ Google Scholar ] [ CrossRef ]
• Athey, Susan, Raj Chetty, and Guido Imbens. 2020. Combining Experimental and Observational Data to Estimate Treatment Effects on Long Term Outcomes. arXiv arXiv:2006.09676. [ Google Scholar ]
• Hall, Peter, and Joel L. Horowitz. 2005. Nonparametric methods for inference in the presence of instrumental variables. Annals of Statistics 33: 2904–29. [ Google Scholar ] [ CrossRef ]
• Horowitz, Joel L. 2011. Applied nonparametric instrumental variables estimation. Econometrica 79: 347–94. [ Google Scholar ] [ CrossRef ]
• Imbens, Guido W., and Donald B. Rubin. 2015. Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction . Cambridge: Cambridge University Press. [ Google Scholar ]
• James, William, and Charles Stein. 1992. Estimation with quadratic loss. In Breakthroughs in Statistics: Foundations and Basic Theory . New York: Springer, pp. 443–60. [ Google Scholar ]
• Kallus, Nathan, Aahlad Manas Puli, and Uri Shalit. 2018. Removing hidden confounding by experimental grounding. Paper presented at the 32nd International Conference on Neural Information Processing Systems, NIPS’18, Montréal, QC, Canada, December 2–8; Red Hook: Curran Associates Inc., pp. 10911–20. [ Google Scholar ]
• Newey, Whitney K., and James L. Powell. 2003. Instrumental variable estimation of nonparametric models. Econometrica 71: 1565–78. [ Google Scholar ] [ CrossRef ]
• Rosenbaum, Paul R., and Donald B. Rubin. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70: 41–55. [ Google Scholar ] [ CrossRef ]
• Rosenman, Evan T. R., Guillaume Basse, Art B. Owen, and Mike Baiocchi. 2023. Combining observational and experimental datasets using shrinkage estimators. Biometrics 79: 2961–73. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Rubin, Donald B. 1974. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology 66: 688. [ Google Scholar ] [ CrossRef ]
• Wang, Guihua, Jun Li, and Wallace J. Hopp. 2022. An instrumental variable forest approach for detecting heterogeneous treatment effects in observational studies. Management Science 68: 3399–18. [ Google Scholar ] [ CrossRef ]
• Wu, Lili, and Shu Yang. 2022. Integrative r -learner of heterogeneous treatment effects combining experimental and observational studies. Paper presented at the First Conference on Causal Learning and Reasoning, Eureka, CA, USA, April 11–13. PMLR 177:904–926. [ Google Scholar ]
• Xie, Yu, Jennie E. Brand, and Ben Jann. 2012. Estimating heterogeneous treatment effects with observational data. Sociological Methodology 42: 314–47. [ Google Scholar ] [ CrossRef ] [ PubMed ]

Share and Cite

Han, K.; Wu, H.; Wu, L.; Shi, Y.; Liu, C. Estimating Treatment Effects Using Observational Data and Experimental Data with Non-Overlapping Support. Econometrics 2024 , 12 , 26. https://doi.org/10.3390/econometrics12030026

Han K, Wu H, Wu L, Shi Y, Liu C. Estimating Treatment Effects Using Observational Data and Experimental Data with Non-Overlapping Support. Econometrics . 2024; 12(3):26. https://doi.org/10.3390/econometrics12030026

Han, Kevin, Han Wu, Linjia Wu, Yu Shi, and Canyao Liu. 2024. "Estimating Treatment Effects Using Observational Data and Experimental Data with Non-Overlapping Support" Econometrics 12, no. 3: 26. https://doi.org/10.3390/econometrics12030026

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

Help | Advanced Search

Quantitative Finance > Computational Finance

Title: theoretical and empirical validation of heston model.

Abstract: This study focuses on the application of the Heston model to option pricing, employing both theoretical derivations and empirical validations. The Heston model, known for its ability to incorporate stochastic volatility, is derived and analyzed to evaluate its effectiveness in pricing options. For practical application, we utilize Monte Carlo simulations alongside market data from the Crude Oil WTI market to test the model's accuracy. Machine learning based optimization methods are also applied for the estimation of the five Heston parameters. By calibrating the model with real-world data, we assess its robustness and relevance in current financial markets, aiming to bridge the gap between theoretical finance models and their practical implementations.

Submission history

Access paper:.

• HTML (experimental)
• Other Formats

References & Citations

• Google Scholar
• Semantic Scholar

BibTeX formatted citation

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

• Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

• Type 2 Diabetes
• Heart Disease
• Digestive Health
• Multiple Sclerosis
• Diet & Nutrition
• Health Insurance
• Public Health
• Patient Rights
• Caregivers & Loved Ones
• End of Life Concerns
• Health News
• Thyroid Test Analyzer
• Doctor Discussion Guides
• Hemoglobin A1c Test Analyzer
• Lipid Test Analyzer
• Complete Blood Count (CBC) Analyzer
• What to Buy
• Editorial Process
• Meet Our Medical Expert Board

Novo Nordisk's Experimental Oral Drug Reports 'Remarkable' Weight Loss, Early Data Shows

Bloomberg / Getty Images

Key Takeaways

• In a phase 1 trial, Novo Nordisk's experimental obesity drug amycretin led to up to 13% weight loss in just three months.
• Amycretin targets GLP-1 receptors and a second hormone called amylin.
• Amylin appears to act in the brain, rather than in the gut. It’s possible that amylin can control appetite with fewer gastrointestinal side effects.

An experimental new pill from Novo Nordisk, called amycretin, could cause substantial weight loss in people with obesity.

The once-daily oral medication is in the same class as the blockbuster obesity and diabetes medications, Ozempic and Mounjaro. It targets GLP-1 receptors and stimulates levels of a second hormone called amylin.

In a phase 1 clinical trial, researchers tested if the drug was safe for humans at different doses. Although regulatory approval is still years away, early data suggests that the drug could lead to rapid weight loss. Participants taking amycretin at the highest doses lost up to 13% of their body weight in just three months.

That outcome is “remarkable,” said David Lau, MD, PhD , professor emeritus and director of the Julia McFarlane Diabetes Research Centre at the University of Calgary, who was not involved with the study. "This adds to the current and future investigations and development of weight loss medications to help people to achieve better body weight and, of course, overall health."

How Amycretin Works

Amycretin targets amylin, a hormone secreted by the pancreas. After eating, amylin signals fullness, slows the rate at which food moves through the stomach and limits the release of glucagon, a hormone that increases blood sugar levels.

In a presentation at EASD, Kirsten Raun, DVM , Scientific Vice President at Novo Nordisk, said that amylin could benefit people with obesity by reducing appetite, improving fat-to-lean mass ratio, supporting bone formation, lowering blood pressure, improving lipid profile, and more.

Combining amylin and GLP-1 receptor agonists into one medication could stimulate weight loss in more ways than semaglutide, which targets GLP-1 alone.

“The data are very consistent that amylin agonists actually affect appetite centrally in the brain. But what we don’t know is the exact location in the brain where amylin works,” Lau said. “If amylin acts differently from the GLP-1 receptor agonists, there may be an additive effect on appetite regulation. If they act on different parts of the brain, they may have better effect.”

What We Know From the Clinical Trial

The trial included 144 participants between the ages of 18 and 55 years who had a BMI of 25 to 40 and were otherwise considered healthy.

Novo Nordisk used the same technology to create an oral form of amycretin as it uses for Rybelsus, the oral version of semaglutide.

The researchers tested multiple dosing regimens. In a presentation of the data, Agnes Gasiorek, PhD , senior clinical pharmacology specialist at Novo Nordisk, highlighted a portion of the study in which the dosage for some participants about doubled every two to three weeks for three months.

One group ended with a dose of 50 milligrams. They saw an average 10.4% body weight loss by the end of the trial. A group that took twice that dose lost 13.1% of their body weight during that time. The placebo group, meanwhile, lost 1.2% of their body weight.

That outcome “really is remarkable for an orally delivered biologic,” Gasiorek said.

Gasiorek said it’s too early to compare the benefits of amycretin with semaglutide or other obesity medications. Future studies will test how amycretin affects the body over a longer period.

One challenge of developing an oral version is making the drug effective at the lowest dose possible. About 1% of oral semaglutide is bioavailable, so people need to take a much higher dose of the medication to receive the same effect as an injectable dose. Novo Nordisk hasn’t yet studied what percentage of amycretin is absorbed orally. 

Striving for Weight Loss With Fewer GI Side Effects

A common complaint among people who take GLP-1-based drugs is the discomfort of gastrointestinal side effects. Research by Lau and his team suggests that targeting amylin can increase feelings of satiety and fullness while reducing the risk of gastrointestinal side effects.

In the trial data presented last week, Novo Nordisk reported that most people experienced mild to moderate gastrointestinal side effects, such as nausea, vomiting, and abdominal pain. The likelihood of those side effects increased as participants started taking higher doses of amycretin.

Amylin appears to act in the brain, rather than in the gut. Scientists are still learning about which parts of the brain are responsible for nausea and which parts amylin stimulates, but it’s possible that amylin helps control appetite while signaling less gastrointestinal discomfort.

“The effect of nausea and the effect of weight loss are completely separate, so nausea in and by itself does not necessarily lead to weight loss. Some people can lose weight without experiencing nausea, and some people experiencing weight loss also can experience nausea,” Lau said.

For companies competing to create the most tolerable and effective obesity and diabetes medications, Lau said a primary goal is to understand how to target certain hormones to induce weight loss without nausea.

Where Amycretin Could Fit Into Obesity Care

Novo Nordisk will advance the once-daily oral tablet to phase 2 clinical trials. The company is also testing a subcutaneous injection version of the drug.

Lau said that GLP-1 receptor agonists will continue to be the “major ingredient” for future anti-obesity medications. Drugs that also target another hormone or two have the potential to be more potent.

For instance, Eli Lilly’s tirzepatide stimulates GLP-1 and GIP receptors and causes greater average weight loss in clinical trials than semaglutide, which targets GLP-1 alone. A drug in phase 3 clinical trials, called retatrutide, targets three different hormones with promising effects.

“Obesity, as a condition, is actually very heterogeneous. Some people respond tremendously, other people don’t,” Lau said.

Someone who is a “super responder” to GLP-1 drugs may lose sufficient weight when taking a drug that contains amylin with even fewer side effects than they may experience on semaglutide, for instance.

What This Means For You

If you're interested in obesity management options, talk to a health provider about your treatment options. You can use this tool from the Obesity Medicine Association to find an obesity medicine provider.

Gasiorek A, Kirkeby K, Toubro S, et al. Safety, tolerability and weight reduction findings of oral amycretin: a novel amylin and glucagon-like peptide-1 receptor co-agonist, in a first-in-human study . Paper presented at: 60th Annual Meeting of the European Association for the Study of Diabetes; September 9-13, 2024, Madrid, Spain.

Brayden DJ, Hill TA, Fairlie DP, Maher S, Mrsny RJ. Systemic delivery of peptides by the oral route: formulation and medicinal chemistry approaches . Adv Drug Deliv Rev . 2020;157:2-36. doi:10.1016/j.addr.2020.05.007

Enebo LB, Berthelsen KK, Kankam M, et al. Safety, tolerability, pharmacokinetics, and pharmacodynamics of concomitant administration of multiple doses of cagrilintide with semaglutide 2·4 mg for weight management: a randomised, controlled, phase 1b trial . Lancet . 2021;397(10286):1736-1748. doi:10.1016/S0140-6736(21)00845-X

Sanyal AJ, Kaplan LM, Frias JP, et al. Triple hormone receptor agonist retatrutide for metabolic dysfunction-associated steatotic liver disease: a randomized phase 2a trial . Nat Med . 2024;30(7):2037-2048. doi:10.1038/s41591-024-03018-2

By Claire Bugos Bugos is a senior news reporter at Verywell Health. She holds a bachelor's degree in journalism from Northwestern University.