The DOI matches its Crossref record; 82 of the 82 references it cites were found in a registry or an index.
Discovering Physical Concepts with Neural Networks
Open accessAbstract
Despite the success of neural networks at solving concrete physics problems, their use as a general-purpose tool for scientific discovery is still in its infancy. Here, we approach this problem by modeling a neural network architecture after the human physical reasoning process, which has similarities to representation learning. This allows us to make progress towards the long-term goal of machine-assisted scientific discovery from experimental data without making prior assumptions about the system. We apply this method to toy examples and show that the network finds the physically relevant parameters, exploits conservation laws to make predictions, and can help to gain conceptual insights, e.g., Copernicus' conclusion that the solar system is heliocentric.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019) · Journal of Computational Physics · doi:10.1016/j.jcp.2018.10.045 · Cited by 20,384
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
Advances in Neural Information Processing Systems 29
Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001
Quantum Computation and Quantum Information: 10th Anniversary Edition
Nielsen, M. A., & Chuang, I. L. (2012) · doi:10.1017/cbo9780511976667 · Cited by 8,436
Research works printed but not linked: 6
- J. B. Hamrick, 2011, Proceedings of the 33rd Annual Conference of the Cognitive Science Society Austin, TX
- J. P. Crutchfield, 1987, Complex Syst., 1, 417
- C. Bates, 2015, Proceedings of the 37th Annual Conference of the Cognitive Science Society, Pasadena, CA
- J. Wu, 2015, Advances in Neural Information Processing Systems 28
- I. Higgins, 2017, ICLR, 2, 1
- 2004, Lecture Notes in Physics
Cited by
Physics-informed machine learning
Karniadakis, G. E., Kevrekidis, I. G., Lu, L., Perdikaris, P., Wang, S., & Yang, L. (2021) · Nature Reviews Physics · doi:10.1038/s42254-021-00314-5 · Cited by 8,718
Explainable machine learning for scientific insights and discoveries
Roscher, R., Bohn, B., Duarte, M. F., & Garcke, J. (2020) · IEEE Access · doi:10.1109/access.2020.2976199 · Cited by 1,040
“Reference [100] introduces SciNet, a modified variational autoencoder that learns a representation from experimental data and uses the learned representation to derive physical concepts from it rather than from the experimental input”
BackgroundHart, G. L. W., Mueller, T., Toher, C., & Curtarolo, S. (2021) · Nature Reviews Materials · doi:10.1038/s41578-021-00340-w · Cited by 703
Polino, E., Valeri, M., Spagnolo, N., & Sciarrino, F. (2020) · AVS Quantum Science · doi:10.1116/5.0007577 · Cited by 414
Combustion machine learning: Principles, progress and prospects
Ihme, M., Chung, W. T., & Mishra, A. A. (2022) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2022.101010 · Cited by 265
Metadata
- Authors
- Iten, R., Metger, T., Wilming, H., del Rio, L., & Renner, R.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2020-01-08
- Volume
- 124
- Issue
- 1
- Pages
- 010508-010508
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2884775584
- Cited by
- 458
- References
- 82
- FWCI
- 35.17
- Type
- journal article
- Language
- en
- Open access
- green
- Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (200020_165843)
- Foundational Questions Institute
- Eidgenössische Technische Hochschule Zürich
- Swiss National Supercomputing Centre
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Discovering Physical Concepts with Neural Networks. https://app.cochat.ai/papers/10.1103/physrevlett.124.010508
- The paper
- Iten, R., Metger, T., Wilming, H., del Rio, L., & Renner, R. (2020). Discovering Physical Concepts with Neural Networks. Physical Review Letters. https://doi.org/10.1103/physrevlett.124.010508
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
