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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
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Machine LearningLicense
MIT LicenseFollow NeuralPDE.jl
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