The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
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Updated
Jun 27, 2024 - Julia
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
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
Finite element toolbox for Julia
Implementation of the finite volume method in 1D.
Solving Universal Differential Equations in Julia
High Level API Finite Element Methods based on ExtendableGrids and ExtendableFEMBase
Parallel distributed-memory version of Gridap
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
1D and 2D Wave simulation from scratch
Finite Element tools in Julia
Solver for 1D nonlinear partial differential equations in Julia based on the collocation method of Skeel and Berzins and providing an API similar to MATLAB's pdepe
Automatic Finite Difference PDE solving with Julia SciML
Julia package for function approximation
Grid-based approximation of partial differential equations in Julia
Solution of nonlinear multiphysics partial differential equation systems using the Voronoi finite volume method
High-level model-order reduction to automate the acceleration of large-scale simulations
DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia
Solver for two-dimensional conservation equations using the finite volume method in Julia.
A scientific machine learning (SciML) wrapper for the FEniCS Finite Element library in the Julia programming language
Adaptive P/ODE numerics with Grassmann element TensorField assembly
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