Inherently interpretable machine learning solutions to differential equations
Author:
Funder
Army Research Laboratory
Defense Sciences Office, DARPA
Sandia National Laboratories
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,General Engineering,Modeling and Simulation,Software
Link
https://link.springer.com/content/pdf/10.1007/s00366-023-01915-7.pdf
Reference32 articles.
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2. Raissi M, Perdikaris P, Karniadakis G (2019) Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J Comput Phys 378:686–707. https://doi.org/10.1016/j.jcp.2018.10.045
3. Karpatne A, Atluri G, Faghmous JH, Steinbach M, Banerjee A, Ganguly A, Shekhar S, Samatova N, Kumar V (2017) Theory-guided data science: a new paradigm for scientific discovery from data. IEEE Trans Knowl Data Eng 29(10):2318–2331. https://doi.org/10.1109/TKDE.2017.2720168
4. Raissi M, Perdikaris P, Karniadakis GE (2017) Physics informed deep learning (part I): data-driven solutions of nonlinear partial differential equations. arXiv preprint arXiv:1711.10561. https://doi.org/10.48550/arXiv.1711.10561
5. Heckman NM, Ivanoff TA, Roach AM, Jared BH, Tung DJ, Brown-Shaklee HJ, Huber T, Saiz DJ, Koepke JR, Rodelas JM, Madison JD, Salzbrenner BC, Swiler LP, Jones RE, Boyce BL (2020) Automated high-throughput tensile testing reveals stochastic process parameter sensitivity. Mater Sci Eng A 772:138632. https://doi.org/10.1016/j.msea.2019.138632
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