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Artificial intellignece in physics

Summary

The course is focused on neureal networks capable of simulation of physical phenomena. It introduces commonly used types of these networks and focuses particularly on Physically informed neural networks (PINNs). The construction, advantages and weaknesses of particular varinats of PINNs and their usage in practise are discussed in detail.

Literature

[1] Steve Brunton. Physics Informed Machine Learning. Accessed: 2024-08-01.
2024. url: https://www.youtube.com/playlist?list=PLMrJAkhIeNNQ0BaKuBKY43k4xMo6NSbBa.
[2] ETH Zürich CAMLab. ETH Zürich — Deep Learning in Scientific Compu-
ting 2023. Accessed: 2024-08-01. 2024. url: https://www.youtube.com/
watch?v=y6wHpRzhhkA&list=PLJkYEExhe7rYY5HjpIJbgo-tDZ3bIAqAm.
[3] Zhongkai Hao et al. Physics-Informed Machine Learning: A Survey on Pro-
blems, Methods and Applications. 2023. arXiv: 2211.08064 [cs.LG]. url:
https://arxiv.org/abs/2211.08064.
[4] Stefan Kollmannsberger et al. Deep Learning in Computational Mechanics:
An Introductory Course. Springer Cham, 2021. isbn: 978-3-030-76586-6 .
doi: 10.1007/978-3-030-76587-3.
[5] Karet Pentil. PINN (Physics-Informed Neural Networks). Accessed: 2024-
08-01. 2021. url: https://www.youtube.com/playlist?list=PLXmYoJbJ848pkMm9NGZZKXUQJ8XWIXZX8
[6] Nils Thuerey et al. Physics-based Deep Learning. 2022. arXiv: 2109.05237 [cs.LG].
url: https://arxiv.org/abs/2109.05237.
[7] Genki Yagawa a Atsuya Oishi. Computational Mechanics with Deep Lear-
ning: An Introduction. Lecture Notes on Numerical Methods in Enginee-
ring and Sciences. Springer Cham, 2022. isbn: 978-3-031-11846-3 . doi:
10.1007/978-3-031-11847-0.
[8] M. Raissi, P. Perdikaris a G.E. Karniadakis. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 2019. doi: 10.1016/j.jcp.2018.10.045.
[9] Edward Small. An Analysis of Physics-Informed Neural Networks. arXiv:2303.02890 [cs.LG], 2023. url: https://arxiv.org/abs/2303.02890

Advised literature

[1] Ricardo Vinuesa. Physics-informed neural networks for fluid mechanics.
Accessed: 2024-08-01. 2024. url: https://www.youtube.com/watch?v=EHrgSPHZG3Y.
[2] Timon Rabczuk and Klaus-Jürgen Bathe. Machine Learning in Modeling and Simulation: Methods and Applications. Springer Cham, 2023. isbn: 978-3-031-36643-6 . doi: 10.1007/978-3-031-36644-4.
[3] Steven L. Brunton a J. Nathan Kutz. Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, 2023. isbn: 978-1009098489.
[4] Dimitrios Angelis et al. Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives. Springer Cham, 2023. isbn: 978-3-030-99226-2 .


Language of instruction čeština, angličtina
Code 653-3177
Abbreviation UIF
Course title Artificial intellignece in physics
Coordinating department Department of Materials Engineering and Recycling
Course coordinator Ing. Dalibor Javůrek, Ph.D.