References

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.

CT21

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/.

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.

Gol90

H Goldstine. A history of scientific computing. ACM, 1990.

HKT19

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/.

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/.

KB14

Diederik P Kingma and Jimmy Ba. Adam: a method for stochastic optimization. arXiv:1412.6980, 2014.

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.

KUT20

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/.

KSH12

Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems. 2012.

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.

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.

Qur19

Mohammed Al Quraishi. Alphafold at casp13. Bioinformatics, 35(22):4862–4865, 2019.

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.

RPK19

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.

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.

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.

SWD+17

John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv:1707.06347, 2017.

SSS+17

David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, and others. Mastering the game of Go without human knowledge. Nature, 2017.

Sto14

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.

SB18

Richard S Sutton and Andrew G Barto. Reinforcement learning: An introduction. MIT press, 2018.

TWPH20

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/.

TSSP17

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.

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/.

UPTK19

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/.

WBT19

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/.

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/.

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/.