Grid-based approximation of partial differential equations in Julia
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Updated
Dec 23, 2024 - Julia
Grid-based approximation of partial differential equations in Julia
Python package for solving partial differential equations using finite differences.
Implementation of the paper "Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism" [AAAI-MLPS 2021]
Geophysical fluid dynamics pseudospectral solvers with Julia and FourierFlows.jl.
Code for the paper "Poseidon: Efficient Foundation Models for PDEs"
Rensselaer's Optimistic Simulation System
NRPy+, BlackHoles@Home, SENRv2, and the NRPy+ Jupyter Tutorial: Python-Based Code Generation for Numerical Relativity... and Beyond!
Generative Pre-Trained Physics-Informed Neural Networks Implementation
A Python library for solving any system of hyperbolic or parabolic Partial Differential Equations. The PDEs can have stiff source terms and non-conservative components.
Three Dimensional Magnetohydrodynamic(MHD) pseudospectral solvers written in julia with FourierFlows.jl
FEM toolbox with automatic code generation, HPC support, and more.
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
Spatial bio-chemical reaction model editor and simulator
Simian Process Oriented Conservative JIT PDES from LANL
Physics-informed neural networks (PINNs)
Deep-learning model for optimised proper orthogonal decomposition of non-linear, hyperbolic, parametric PDEs based on a pre-processing method of the full-order solutions
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