NeuralPDE.jl is a Julia library for solving partial differential equations (PDEs) using physics-informed neural networks and scientific machine learning. Built on top of the SciML ecosystem, it provides a flexible and composable interface for defining PDEs and training neural networks to approximate their solutions. NeuralPDE.jl enables hybrid modeling, data-driven discovery, and fast PDE solvers in high dimensions, making it suitable for scientific research and engineering applications.

Features

  • Solves PDEs using physics-informed neural networks (PINNs)
  • Supports symbolic PDE definition via Symbolics.jl
  • Integrates with Flux.jl and SciML solvers
  • Enables hybrid and data-driven modeling
  • Handles high-dimensional and nonlinear systems
  • GPU acceleration and automatic differentiation support

Project Samples

NeuralPDE.jl Screenshot 1

Project Activity

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Categories

Machine Learning

License

MIT License

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Additional Project Details

Operating Systems

Linux, Mac, Windows

Programming Language

Julia

Related Categories

Julia Machine Learning Software

Registered

2025-07-21
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