NeuralPDE.jl and heat-pinn
NeuralPDE.jl is a general-purpose PINN framework that heat-pinn uses as a reference implementation or builds upon for the specialized case of 2D steady-state heat equations, making them complements in a hierarchical ecosystem where the former provides infrastructure and the latter demonstrates application.
About NeuralPDE.jl
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.
About heat-pinn
314arhaam/heat-pinn
A Physics-Informed Neural Network to solve 2D steady-state heat equations.
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