Combustion machine learning, Explainable machine learning in materials science for polycrystalline materials
Scientific understanding: "It will be exciting to see how these approaches, for example, combined with methods such as causal inference, can be improved to propose reasonable physical models of unknown systems that advance scientific understanding."
equation discovery
- For a collectively oscillatory behaviour model of rumbles: [Rantsiou, 2024] from Harvard University.
- For a core-collapse supernovae model: [Soto& Villar, 2023, NeurIPS-W] from Harvard&Smithsonian.
- For a rogue wave model: [Häfner et al., 2023, PNAS] from Pasteur Labs+University of Copenhagen+University of Victoria.
- For a galaxy-halo connection model: [Delgado et al., 2022, MNRAS] from Harvard&Smithsonian+New York University+Institute for Advanced Study, Princeton+Durham University+Flatiron Institute+Princeton University+Carnegie Mellon University.
- For the CAMELS (Cosmology and Astrophysics with Machine-learning Simulations) project: [Villaescusa-Navarro et al., 2021, ApJ] from Princeton University+Flatiron Institute+University of Connecticut+Columbia University+Rutgers University+University of Edinburgh+Max-Planck-Institut für Astronomie+Harvard & Smithsonian+Universität Heidelberg+University of Florida+Tufts University+Carnegie Mellon University+University of the Western Cape+New York University+Cornell University.
- https://mrbuche.com/files/applying-genetic-programming-symbolic-regression-to-solid-mechanics.pdf
- Long, F.X., Vermetten, D., Kononova, A.V., Kalkreuth, R., Yang, K., Bäck, T. and van Stein, N., 2023. Challenges of ELA-guided function evolution using genetic programming. arXiv preprint arXiv:2305.15245.
- Cranmer, M., 2023. Interpretable machine learning for science with PySR and SymbolicRegression.jl. arXiv preprint arXiv:2305.01582.
- La Cava, W., Orzechowski, P., Burlacu, B., de França, F.O., Virgolin, M., Jin, Y., Kommenda, M. and Moore, J.H., 2021. Contemporary symbolic regression methods and their relative performance. arXiv preprint arXiv:2107.14351.
- Kötzing, T., Lagodzinski, J.G., Lengler, J. and Melnichenko, A., 2020. Destructiveness of lexicographic parsimony pressure and alleviation by a concatenation crossover in genetic programming. Theoretical Computer Science, 816, pp.96-113.
- Langdon, W.B. and Poli, R., 2013. Foundations of genetic programming. Springer Science & Business Media.
- Koza, J.R., 1989, August. Hierarchical genetic algorithms operating on populations of computer programs. In Proceedings of International Joint Conference on Artificial Intelligence (pp. 768-774).
- https://github.com/hengzhe-zhang/awesome-genetic-programming (This provides an open-access collection of some research papers on GP.)
- https://astroautomata.com/PySR/ | https://github.com/MilesCranmer/PySR
- https://www.nature.com/articles/s41586-023-06221-2
- Kelly, S., Park, D.S., Song, X., McIntire, M., Nashikkar, P., Guha, R., Banzhaf, W., Deb, K., Boddeti, V.N., Tan, J. and Real, E., 2023. Discovering adaptable symbolic algorithms from scratch. .
- https://www.researchsquare.com/article/rs-3307450/v1
- La Cava, W., Singh, T.R., Taggart, J., Suri, S. and Moore, J.H., 2018, September. Learning concise representations for regression by evolving networks of trees. In International Conference on Learning Representations.