Ceyron/pdequinox
Neural Emulator Architectures in JAX.
This tool helps researchers and engineers rapidly predict the behavior of complex physical systems governed by Partial Differential Equations (PDEs). You input system conditions or forces and receive predictions of physical states like displacement or temperature. It's for computational scientists, physicists, and engineers who work with simulations and need faster, data-driven solutions.
Available on PyPI.
Use this if you need to quickly approximate solutions to PDEs across various spatial dimensions and boundary conditions using pre-built neural network architectures.
Not ideal if you require exact analytical solutions or if your physical system does not fit a uniform Cartesian grid discretization.
Stars
24
Forks
—
Language
Python
License
MIT
Category
Last pushed
Feb 20, 2026
Commits (30d)
0
Dependencies
4
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