thunil/Physics-Based-Deep-Learning resource

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1908 stars · 317 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A curated collection of links to research works on physics-based deep learning (PBDL), combining physical modeling with deep learning techniques, with emphasis on fluid flow and Navier-Stokes problems. It accompanies a comprehensive digital PBDL book from the I15 lab at TUM.

Use cases

• find papers on deep learning for physics simulation
• learn differentiable physics methods
• research neural network approaches to fluid simulation
• study physics-informed loss functions for training neural networks
• find resources on inverse problems in physical systems
• get started with physics-based deep learning via the PBDL book

When to choose

• you need a curated reading list of physics + deep learning research
• you want to learn about differentiable physics and neural fluid simulation
• you are looking for the accompanying PBDL book and overview material

When to avoid

• you need runnable code or a software library rather than paper links
• you need machine learning for physics outside deep learning (e.g., classical ML)
• you need an exhaustive bibliography of all groups in the field

Facets

learning-resource · maturity active

machine-learning deep-learning simulation documentation machine-learning deep-learning simulation tutorials awesome-lists cross-platform awesome-list physics-based-deep-learning differentiable-physics fluid-simulation research-papers navier-stokes physics

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