tum-pbs/pbdl-book resource

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1361 stars · 214 forks observed · 2026-08-28

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

An open-source Jupyter book on combining deep learning with physical simulations and numerical methods, readable online or as a PDF. It includes interactive notebooks covering physics-informed losses, differentiable simulations, diffusion-based generative models, and reinforcement learning for PDE problems.

Use cases

• learn how to apply deep learning to solve PDE problems
• train neural networks to predict fluid flow around airfoils
• understand differentiable simulations and physics-informed training
• build probabilistic surrogate models with diffusion models
• learn to combine neural networks with numerical simulators for inverse problems
• find a textbook on physics-based deep learning with hands-on notebooks

When to choose

• you want a practical, notebook-driven introduction to ML for physical simulations
• you need to learn physics-informed neural networks, differentiable simulation, or diffusion-based surrogate modeling
• you have some deep learning background and want to apply it to scientific computing

When to avoid

• you need a beginner introduction to deep learning or numerical simulation fundamentals
• you want a comprehensive survey of research papers in the field
• you need production-ready software rather than educational material

Facets

learning-resource · maturity active

machine-learning deep-learning simulation nlp machine-learning deep-learning simulation artificial-intelligence tutorials python cross-platform jupyter-book physics-based-deep-learning pde-solvers differentiable-simulation diffusion-models scientific-computing fluids generative-ai

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