idrl-lab/PINNpapers resource

Health v2 · maintenance only

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Adoption not part of the score

1533 stars · 208 forks observed · 2026-08-28

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

A curated reading list of must-read papers on Physics-Informed Neural Networks (PINNs), maintained by the IDRL lab. It organizes representative works by topic (models, parallelization, acceleration, transfer learning, uncertainty quantification, applications, and analysis) and includes a BibTeX-to-markdown conversion script.

Use cases

• find papers on physics-informed neural networks
• get started learning about PINNs for scientific computing
• survey PINN applications and theory before starting research
• find open-source PINN software libraries like DeepXDE
• keep up with recent PINN acceleration and parallelization work
• build a bibliography for a PINN literature review

When to choose

• you need a curated, categorized overview of the PINN research landscape
• you are a researcher or student surveying physics-informed deep learning literature
• you want pointers to both papers and accompanying code repositories

When to avoid

• you need a runnable PINN library rather than a paper list
• you need an exhaustive, automatically updated paper index instead of a curated selection
• you need tutorials or code examples rather than references to papers

Facets

learning-resource · maturity maintenance

machine-learning deep-learning simulation documentation machine-learning deep-learning simulation tutorials awesome-lists python awesome-list physics-informed-neural-networks pinn papers scientific-computing reading-list bibliography

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For agents

markdown · JSON · MCP: product_card(name="idrl-lab/PINNpapers")