SciML/NeuralPDE.jl

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1220 stars · 249 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

NeuralPDE.jl is a Julia library of physics-informed neural network (PINN) solvers for ordinary, stochastic, and partial differential equations. It automatically constructs physics-informed loss functions from a symbolic interface and integrates with Flux.jl, Lux.jl, and NeuralOperators.jl for GPU-accelerated scientific machine learning.

Use cases

• solve partial differential equations with physics-informed neural networks
• fit a neural network to both physical laws and experimental data
• solve ODEs and SDEs using neural network solvers
• train PINNs with automatic loss construction from symbolic equations
• combine DeepONet or Fourier neural operators with physics-informed losses
• accelerate PDE simulation with GPU-powered training

When to choose

• you work in Julia and need PINN-based differential equation solvers
• you want to mix equation solving with data fitting in a scientific machine learning workflow
• you need automated physics-informed loss functions from a high-level symbolic interface
• you want neural operator methods like DeepONets combined with physics constraints

When to avoid

• you need classical finite-element or finite-difference PDE solvers rather than neural approaches
• you work primarily in Python rather than Julia
• you need a lightweight tool without a machine learning stack
• your equations are simple enough that classical numerical solvers are faster and more accurate

Facets

library · maturity active

machine-learning simulation deep-learning machine-learning simulation cross-platform pinn physics-informed-neural-networks pde-solver ode-solver sciml neural-operators flux lux solver scientific-machine-learning differential-equations julia gpu

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