idrl-lab/PINNpapers
A curated collection of must-read papers and software tools for Physics-Informed Neural Networks.
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This repository aggregates representative research papers on Physics-Informed Neural Networks (PINNs), a deep learning approach that embeds physical laws into neural network training to solve differential equations. It also references software libraries such as DeepXDE, SciANN, and NVIDIA SimNet that implement PINN methodologies for scientific computing applications including computational physics and uncertainty quantification.
Frequently asked
- What is idrl-lab/PINNpapers?
- A curated collection of must-read papers and software tools for Physics-Informed Neural Networks.
- Is PINNpapers open source?
- Yes — idrl-lab/PINNpapers is open source, released under the MIT license.
- What language is PINNpapers written in?
- idrl-lab/PINNpapers is primarily written in Python.
- How popular is PINNpapers?
- idrl-lab/PINNpapers has 1.5k stars on GitHub.
- Where can I find PINNpapers?
- idrl-lab/PINNpapers is on GitHub at https://github.com/idrl-lab/PINNpapers.