1. [AAC+19] Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej, Mateusz Litwin, Bob McGrew, and others. Solving rubik's cube with a robot hand. arXiv: 1910.07113, 2019.
2. [CCHT 21]Li-Wei Chen, Berkay A Cakal, Xiangyu Hu, and Nils Thuerey. Numerical investigation of minimum drag profiles in laminar flow using deep learning surrogates. Journal of Fluid Mechanics, 2021. URL: https://ge.in.tum.de/publications/2020-chen-dl-surrogates/.
3. [CT 21]Li-Wei Chen and Nils Thuerey. Towards high-accuracy deep learning inference of compressible turbulent flows over aerofoils. In arXiv. 2021. URL: https://ge.in.tum.de/publications/.
4. [CTS+21]Mengyu Chu, Nils Thuerey, Hans-Peter Seidel, Christian Theobalt, and Rhaleb Zayer. Learning Meaningful Controls for Fluids. ACM Trans. Graph., 2021. URL: https://people.mpi-inf.mpg.de/~mchu/gvv-den2vel/den2vel.html.
5. [Gol 90]H Goldstine. A history of scientific computing. ACM, 1990. ^c6019b
6. [HKT 19]Philipp Holl, Vladlen Koltun, and Nils Thuerey. Learning to control pdes with differentiable physics. In International Conference on Learning Representations. 2019. URL: https://ge.in.tum.de/publications/2020-iclr-holl/.
7. [HKT 21]Philipp Holl, Vladlen Koltun, and Nils Thuerey. Physical gradients and scale-invariant physics for deep learning. In arXiv: 2109.15048. 2021. URL: https://arxiv.org/abs/2109.15048.
8. [KAT+19]Byungsoo Kim, Vinicius C Azevedo, Nils Thuerey, Theodore Kim, Markus Gross, and Barbara Solenthaler. Deep Fluids: A Generative Network for Parameterized Fluid Simulations. Comp. Grap. Forum, 38 (2): 12, 2019. URL: http://www.byungsoo.me/project/deep-fluids/.
9. [KB 14]Diederik P Kingma and Jimmy Ba. Adam: a method for stochastic optimization. arXiv: 1412.6980, 2014.
10. [KSA+21]Dmitrii Kochkov, Jamie A Smith, Ayya Alieva, Qing Wang, Michael P Brenner, and Stephan Hoyer. Machine learning–accelerated computational fluid dynamics. Proceedings of the National Academy of Sciences, 2021.
11. [KUT 20]Georg Kohl, Kiwon Um, and Nils Thuerey. Learning similarity metrics for numerical simulations. International Conference on Machine Learning, 2020. URL: https://ge.in.tum.de/publications/2020-lsim-kohl/.
12. [KSH 12]Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems. 2012.
13. [LCT 22]Bjoern List, Liwei Chen, and Nils Thuerey. Learned turbulence modelling with differentiable fluid solvers. In arXiv: 2202.06988. 2022. URL: https://ge.in.tum.de/publications/.
14. [MLA+19]Rajesh Maingi, Arnold Lumsdaine, Jean Paul Allain, Luis Chacon, SA Gourlay, and others. Summary of the fesac transformative enabling capabilities panel report. Fusion Science and Technology, 75 (3):167–177, 2019. ^5502d2
15. [OMalleyBK+16]Peter JJ O’Malley, Ryan Babbush, Ian D Kivlichan, Jonathan Romero, Jarrod R McClean, Rami Barends, Julian Kelly, Pedram Roushan, Andrew Tranter, Nan Ding, and others. Scalable quantum simulation of molecular energies. Physical Review X, 6 (3): 031007, 2016. ^a41ddb
16. [Qur 19]Mohammed Al Quraishi. Alphafold at casp 13. Bioinformatics, 35 (22):4862–4865, 2019.
17. [RWC+19]Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. Language models are unsupervised multitask learners. OpenAI blog, 1 (8): 9, 2019.
18. [RPK 19]Maziar Raissi, Paris Perdikaris, and George Karniadakis. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378:686–707, 2019.
19. [SGGP+20]Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, and Peter Battaglia. Learning to simulate complex physics with graph networks. In International Conference on Machine Learning, 8459–8468. 2020.
20. [SHT 22]Patrick Schnell, Philipp Holl, and Nils Thuerey. Half-inverse gradients for physical deep learning. In ICLR. 2022. URL: https://github.com/tum-pbs/half-inverse-gradients.
21. [SML+15]John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel. High-dimensional continuous control using generalized advantage estimation. arXiv: 1506.02438, 2015.
22. [SWD+17]John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv: 1707.06347, 2017.
23. [SSS+17]David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, and others. Mastering the game of Go without human knowledge. Nature, 2017.
24. [Sto 14]Thomas Stocker. Climate change 2013: the physical science basis: Working Group I contribution to the Fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge university press, 2014. ^49388b
25. [SB 18]Richard S Sutton and Andrew G Barto. Reinforcement learning: An introduction. MIT press, 2018.
26. [TWPH 20]Nils Thuerey, Konstantin Weissenow, Lukas Prantl, and Xiangyu Hu. Deep learning methods for reynolds-averaged navier–stokes simulations of airfoil flows. AIAA Journal, 58 (1):25–36, 2020. URL: https://ge.in.tum.de/publications/2018-deep-flow-pred/.
27. [TSSP 17]Jonathan Tompson, Kristofer Schlachter, Pablo Sprechmann, and Ken Perlin. Accelerating eulerian fluid simulation with convolutional networks. In Proceedings of Machine Learning Research, 3424–3433. 2017.
28. [UBH+20]Kiwon Um, Robert Brand, Philipp Holl, Raymond Fei, and Nils Thuerey. Solver-in-the-loop: learning from differentiable physics to interact with iterative pde-solvers. Advances in Neural Information Processing Systems, 2020. URL: https://ge.in.tum.de/publications/2020-um-solver-in-the-loop/.
29. [UPTK 19]Benjamin Ummenhofer, Lukas Prantl, Nils Thuerey, and Vladlen Koltun. Lagrangian fluid simulation with continuous convolutions. In International Conference on Learning Representations. 2019. URL: https://ge.in.tum.de/publications/2020-ummenhofer-iclr/.
30. [WBT 19]Steffen Wiewel, Moritz Becher, and Nils Thuerey. Latent-space Physics: Towards Learning the Temporal Evolution of Fluid Flow. Comp. Grap. Forum, 38 (2): 12, 2019. URL: https://ge.in.tum.de/publications/latent-space-physics/.
31. [WKA+20]Steffen Wiewel, Byungsoo Kim, Vinicius C Azevedo, Barbara Solenthaler, and Nils Thuerey. Latent space subdivision: stable and controllable time predictions for fluid flow. Symposium on Computer Animation, 2020. URL: https://ge.in.tum.de/publications/2020-lssubdiv-wiewel/.
32. [XFCT18]You Xie, Erik Franz, Mengyu Chu, and Nils Thuerey. tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow. ACM Trans. Graph., 2018. URL: https://ge.in.tum.de/publications/tempogan/.