This paper presents ‘PINNs-JAX’, an innovative implementation that utilizes the JAX framework to leverage the distinct capabilities of XLA compilers. This approach aims to improve computational efficiency and flexibility within PINN applications. Each subplot corresponds to a problem, with its iteration count displayed at the top.

2024年3月3日 · We conduct a comprehensive comparison of PINNs-JAX against traditional PINN implementations in widely-used frameworks such as TensorFlow V1, TensorFlow V2, and PyTorch, evaluating performance across a variety of six different examples.

The pinn-jax framework is easily extendable to novel types of PINN and systems of differential equations. This can be done by subclassing the PartialDiffEq or OrdinaryDiffEq classes, defined the problems module.

This paper introduces PINNs-JAX, a novel package based on the JAX framework, known for its just-in-time compilation of Python functions into XLA-optimized kernels (Bradbury et al., 2018). Our study conducts a thorough comparison of JAX with other frameworks to identify the most suitable platform for implementing PINNs.

Simple implementation of Physics-Informed Neural Networks for the solution of Partial Differential Equations in JAX (using Equinox and Optax) Resources

PINNs are a modern approach to solving (partial) differential equations (=PDEs) using neural networks based on minimizing a residuum formed with automatic differentiation. This video is a simple...

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2021年8月22日 · Brief about Jax and Autodiff. Mention the usage of jax and its functional style; Mention about the Autodiff cookbook from Jax; Asking them to take a look at Haiku and optax library; About the JaxModel and PINN_Model superclass; Deepchem has recently introduced Jax support for building models and JaxModel superclass is the main API for building ...

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