Simulation of partical systems in physycs

• Sanchez-Gonzalez, A., Godwin, J., Pfaff, T., Ying, R., Leskovec, J., & Battaglia, P. (2020, November). Learning to simulate complex physics with graph networks. In International Conference on Machine Learning (pp. 8459-8468). PMLR.
• Simulator video source: https://sites.google.com/view/learning-to-simulate/
• Project Code & Datasets: https://github.com/deepmind/deepmind-research/tree/master/learning_to_simulate
• Daniel Holden's talk from UbiSoft: https://www.youtube.com/watch?v=sUb0W5_waRI
• SPlisHSPlasH project: https://github.com/InteractiveComputerGraphics/SPlisHSPlasH
• "Data-driven Fluid Simulations using Regression Forests": https://people.inf.ethz.ch/ladickyl/fluid_sigasia15.pdf
• "Latent Space Physics: Towards Learning the Temporal Evolution of Fluid Flow": https://arxiv.org/pdf/1802.10123.pdf
• "Learning to Predict the Cosmological Structure Formation": https://arxiv.org/pdf/1811.06533.pdf
• "Graph Networks as Learnable Physics Engines for Inference and Control": https://arxiv.org/pdf/1806.01242.pdf
• "Relational inductive biases, deep learning, and graph networks": https://arxiv.org/pdf/1806.01261.pdf