SciML/NeuralPDE.jl
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
Leverages symbolic PDE definitions via ModelingToolkit to automatically construct physics-informed loss functions, eliminating manual differentiation and constraint specification. Supports diverse equation types (ODEs, SDEs, RODEs, integro-differential equations) with advanced training techniques including quadrature strategies and adaptive weighting, while maintaining interoperability with Flux.jl, Lux.jl, and NeuralOperators.jl for flexible neural architecture selection and GPU acceleration.
1,175 stars. Actively maintained with 29 commits in the last 30 days.
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Julia
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Last pushed
Feb 25, 2026
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29
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