Ceyron/exponax
Efficient Differentiable n-d PDE Solvers in JAX.
This project helps researchers and engineers quickly simulate and analyze complex physical systems described by partial differential equations (PDEs) in one, two, or three dimensions. You provide the equation's parameters and initial conditions, and it calculates how the system evolves over time. It's ideal for scientists, physicists, and engineers working on fluid dynamics, material science, or reaction-diffusion processes.
163 stars. Available on PyPI.
Use this if you need to simulate partial differential equations efficiently, especially if you require automatic differentiation for tasks like parameter optimization or physics-informed machine learning.
Not ideal if your problem involves non-periodic boundaries, highly irregular domains, or if you prefer traditional finite element methods over spectral methods.
Stars
163
Forks
16
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Mar 02, 2026
Commits (30d)
0
Dependencies
5
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