Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
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Updated
Jun 30, 2024 - Julia
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
Grid-based approximation of partial differential equations in Julia
Finite element toolbox for Julia
Linear operators for discretizations of differential equations and scientific machine learning (SciML)
Julia package for function approximation
DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia
Solution of nonlinear multiphysics partial differential equation systems using the Voronoi finite volume method
Tools for building fast, hackable, pseudospectral partial differential equation solvers on periodic domains
Automatic Finite Difference PDE solving with Julia SciML
A scientific machine learning (SciML) wrapper for the FEniCS Finite Element library in the Julia programming language
Finite Element tools in Julia
"Programming the Finite Element Method" by I M Smith, D V Griffiths and L Margetts
Parallel distributed-memory version of Gridap
High-level model-order reduction to automate the acceleration of large-scale simulations
Partial differential equations using Discrete Exterior Calculus
High Level API Finite Element Methods based on ExtendableGrids and ExtendableFEMBase
Local Fourier Analysis for arbitrary order finite element type operators
Programs modeled after "Numerical Methods for Engineers" by D.V. Griffiths and I.M. Smith
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