Pinns-Torch

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PINNs-Torch is an open-source Research & Education tool that implements Physics-Informed Neural Networks (PINNs) in PyTorch. It significantly enhances performance through CUDA Graphs and JIT Compilers, offering up to 9x speed gains.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is pinns-torch?

PINNs-Torch is a PyTorch-based implementation of Physics-Informed Neural Networks (PINNs), designed to accelerate scientific computing tasks. A key differentiator is its integration of CUDA Graphs and JIT Compilers (TorchScript), which can boost performance by up to nine times compared to earlier TensorFlow v1 implementations. The package is open-source and provides a robust framework for researchers and developers to build and experiment with PINNs. It includes examples for various problems, such as the Navier-Stokes PDE, and offers flexible installation options for both users and contributors. The tool is ideal for those looking to leverage the power of PyTorch for physics-informed machine learning, with a focus on speed and usability.

Best used for

Ideal for researchers and developers who need to implement and accelerate Physics-Informed Neural Networks, solve complex partial differential equations, and conduct scientific simulations. Especially valuable for those seeking significant performance gains in their PyTorch-based physics-informed machine learning projects.

Common actions

implement physics-informed neural networks

accelerate scientific computing

solve partial differential equations

conduct physics-informed research

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Capabilities

Key features

PyTorch PINNs implementation
CUDA Graphs integration
JIT Compilers (TorchScript)
Up to 9x performance gain
Pre-built examples
Automatic data download

Target Audience

professorresearcherdeveloper

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What are the primary performance benefits of using PINNs-Torch?

PINNs-Torch leverages CUDA Graphs and JIT Compilers (TorchScript) to achieve significant performance enhancements. This integration can result in speed gains of up to 9x compared to the original TensorFlow v1 implementation, making it highly efficient for complex simulations.

What are the main dependencies required to install PINNs-Torch?

To install PINNs-Torch, you need PyTorch (version >=2.0.0), PyTorch Lightning (version >=2.0.0), and Hydra (version >=1.3). Once these are in place, you can install PINNs-Torch via pip or by cloning the repository for local development.

Can I use PINNs-Torch for custom problems beyond the provided examples?

Yes, PINNs-Torch is designed for flexibility. You can either implement your training structures using Hydra, following the provided examples, or directly incorporate the package into your custom problem-solving workflow. A tutorial for solving the Schrödinger PDE is available.

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AI Agents & Automation › AI Frameworks & Infra Coding & Development › Open Source & Models

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