pinnrl

Physics-informed neural networks for forward and inverse PDE problems, evaluated against numerical references.

A PINN represents the solution of a PDE as a neural network, trained to satisfy the equation, its boundary and initial conditions, and any measured data. pinnrl provides:

• 10 PDE configurations: heat (1-D, 2-D), wave, Burgers, convection, KdV, Allen–Cahn, Cahn–Hilliard, Black–Scholes and a damped pendulum. Each exact solution is checked by the test suite against its PDE, initial condition and boundary conditions.
• 7 architectures: MLP, ResNet, Fourier features, SIREN, FNO-style, attention and autoencoder.
• Collocation sampling: uniform, stratified, residual-based adaptive (RAD), and an experimental reinforcement-learning bandit.
• Inverse problems: identify PDE parameters from noisy observations.
• The Well: train on 11 high-fidelity simulation datasets.
• A dashboard to launch, monitor and compare runs, and benchmarks with reference solutions and a multi-seed sampling study.

Install

pip install pinnrl              # Python 3.10–3.13; CPU, Apple GPU (mps) or CUDA
pip install 'pinnrl[well]'      # + The Well dataset loaders

Use

pinnrl-train --pde burgers --arch fourier --epochs 2000                          # results in ./runs/
pinnrl-train --pde heat --arch fourier --mode inverse --identify alpha --initial-guess alpha=0.5
pinnrl-dashboard                                                                 # http://127.0.0.1:8050/
pinnrl-benchmark sampling --seeds 10                                             # uniform, stratified, RAD, RL bandit

The Python API is the same pieces: a PDEConfig and PDE class, a PINNModel, and PDETrainer. See Getting started.

When a PINN is the right tool

For a well-posed forward problem in low dimension, classical solvers are faster and far more accurate; the docs show the numbers. PINNs pay off for inverse problems, for combining physics with sparse data, and for mesh-free or high-dimensional settings. The project's direction is the first of these applied to industry: digital twins that estimate unmeasured states of process equipment (first target: evaporators and spray dryers in food-powder plants). See Applications and the Research program.

Documentation

josegarciav.github.io/PINNs-RL-PDE: concepts, the equations, guides, benchmarks, the API reference and the roadmap. The tutorials are in notebooks/, and research studies in experiments/.

Contributing

See CONTRIBUTING.md. In short: uv sync --all-extras, uv run pytest, conventional commits, and physics changes must keep tests/unit_tests/test_physics_consistency.py green.

Citation

pinnrl started as the MSc thesis On Solving Partial Differential Equations (PDEs) Using Physics-Informed Neural Networks (PINNs) (University of the Basque Country UPV/EHU, 2025); the thesis is at docs/assets/thesis.pdf.

@software{garcia_pinnrl,
  author = {Garcia Ventura, Jose},
  title  = {pinnrl: physics-informed neural networks with adaptive collocation sampling},
  url    = {https://github.com/josegarciav/PINNs-RL-PDE}
}

MIT License © Jose Garcia Ventura