Physics-based Deep Learning

• URL: http://arxiv.org/abs/2109.05237v1
• Date: Sat, 11 Sep 2021 09:38:02 GMT
• Title: Physics-based Deep Learning
• Authors: Nils Thuerey and Philipp Holl and Maximilian Mueller and Patrick Schnell and Felix Trost and Kiwon Um
• Abstract summary: Digital book contains a practical and comprehensive introduction to everything related to deep learning in the context of physical simulations. Includes hands-on code examples in the form of Jupyter notebooks to quickly get started. Beyond standard supervised learning from data, we'll look at physical loss constraints, more tightly coupled learning algorithms with differentiable simulations, as well as reinforcement learning and uncertainty modeling.
• Score: 22.248409468073145
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This digital book contains a practical and comprehensive introduction of everything related to deep learning in the context of physical simulations. As much as possible, all topics come with hands-on code examples in the form of Jupyter notebooks to quickly get started. Beyond standard supervised learning from data, we'll look at physical loss constraints, more tightly coupled learning algorithms with differentiable simulations, as well as reinforcement learning and uncertainty modeling. We live in exciting times: these methods have a huge potential to fundamentally change what computer simulations can achieve.

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