lululxvi/deepxde

Health v2 · maintenance only

Adoption not part of the score

4386 stars · 988 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

DeepXDE is a Python library for scientific machine learning and physics-informed learning, built on top of PyTorch, TensorFlow, JAX, and PaddlePaddle. It implements algorithms such as physics-informed neural networks (PINNs) and DeepONet for solving forward and inverse ODEs, PDEs, and learning operators.

Use cases

• solve forward and inverse PDEs with neural networks
• train physics-informed neural networks (PINNs)
• learn operators with DeepONet
• solve fractional and stochastic differential equations
• fit multi-fidelity physics data
• perform inverse design and topology optimization with hard constraints

When to choose

• you need a mature, well-documented PINN library with multiple backend support
• you want to solve ODEs/PDEs or learn operators without writing training loops from scratch
• you need advanced PINN variants like gPINN, fPINN, or adaptive sampling

When to avoid

• you need general-purpose deep learning outside scientific computing
• you require a permissively licensed library (it is LGPL-2.1)
• you need high-performance traditional numerical PDE solvers rather than neural approaches

Facets

library · maturity stable

machine-learning deep-learning simulation math machine-learning deep-learning simulation mathematics python cross-platform pinn deeponet physics-informed-neural-networks pde-solver operator-learning scientific-computing pytorch tensorflow jax paddle scientific-machine-learning

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