74 Open Source Partial Differential Equations Software Projects
Free and open source partial differential equations code projects including engines, APIs, generators, and tools.
Financial Models Numerical Methods 3572 ⭐
Collection of notebooks about quantitative finance, with interactive python code.
Differentialequations.jl 2083 ⭐
Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components
Diffeqflux.jl 647 ⭐
Universal neural differential equations with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods
Scimltutorials.jl 585 ⭐
Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
Simpeg 332 ⭐
Simulation and Parameter Estimation in Geophysics - A python package for simulation and gradient based parameter estimation in the context of geophysical applications.
Neuralpde.jl 523 ⭐
Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
Findiff 234 ⭐
Python package for numerical derivatives and partial differential equations in any number of dimensions.
Riemann_book 171 ⭐
An interactive book about the Riemann problem for hyperbolic PDEs, using Jupyter notebooks. Work in progress.
Diffeqoperators.jl 235 ⭐
Linear operators for discretizations of differential equations and scientific machine learning (SciML)
Pydens 156 ⭐
PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks
Scimlbenchmarks.jl 167 ⭐
Benchmarks for scientific machine learning (SciML) software and differential equation solvers
Diffeqbase.jl 173 ⭐
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
Fourierflows.jl 106 ⭐
Tools for building fast, hackable, pseudospectral partial differential equation solvers on periodic domains
Shallow Water 71 ⭐
Python model solving the shallow water equations (linear momentum, nonlinear continuity)
Fenics.jl 62 ⭐
A scientific machine learning (SciML) wrapper for the FEniCS Finite Element library in the Julia programming language
Pinns Tf2.0 119 ⭐
TensorFlow 2.0 implementation of Maziar Raissi's Physics Informed Neural Networks (PINNs).
2d Elliptic Mesh Generator 36 ⭐
2D orthogonal elliptic mesh generator which solves the Winslow partial differential equations
Ar Pde Cnn 45 ⭐
Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs
Deepflow 24 ⭐
Pytorch implementation of "DeepFlow: History Matching in the Space of Deep Generative Models"
Hdp 26 ⭐
A numerical library for High-Dimensional option Pricing problems, including Fourier transform methods, Monte Carlo methods and the Deep Galerkin method
Pysdc 19 ⭐
pySDC is a Python implementation of the spectral deferred correction (SDC) approach and its flavors, esp. the multilevel extension MLSDC and PFASST.
2d Navier Stokes Solver 16 ⭐
As the field of Computational Fluid Dynamics (CFD) progresses, the fluid flows are more and more analysed by using simulations with the help of high speed computers. In order to solve and analyse these fluid flows we require intensive simulation involving mathematical equations which governs the fluid flow, these are Navier Stokes (NS) equation. Solving these equations has become a necessity as almost every problem which is related to fluid flow analysis call for solving of Navier Stokes equation. These NS equations are partial differential equations so different numerical methods are used to solve these equations. Solving these partial differential equations so different numerical methods requires large amount of computing power and huge amount of memory is in play. Only practical feasible way to solve these equation is write a parallel program to solve them, which can then be run on powerful hardware capable of parallel processing to get the desired results High speed supercomputer will provide us very good performance in terms of reduction in execution time. In paper focus will be on finite volume as a numerical method. We will also see what GPGPU (General-Purpose computing on Graphics Processing Units) is and how we are taking its advantages to solve CFD problems.
Uqpinns Tf2.0 13 ⭐
TensorFlow 2.0 implementation of Yibo Yang, Paris Perdikaris’s adversarial Uncertainty Quantification in Physics Informed Neural Networks (UQPINNs).
Pypde 27 ⭐
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.
Neuralsolvers 46 ⭐
Neural network based solvers for partial differential equations and inverse problems :milky_way:. Implementation of physics-informed neural networks in pytorch.
Mljc Unito Projectx 2020 Public 29 ⭐
Public repository for the proposal “Physics-Informed Machine Learning Simulator for Wildfire Propagation” - MLJC University of Turin - ProjectX2020 Competition (UofT AI)
Neuraloperators.jl 55 ⭐
learning the solution operator for partial differential equations in pure Julia.
Schrodingers Equation Solution By Neural Network Nn 17 ⭐
Artifitial Neural Networks for Solving Ordinary and Partial Differential Equations, in this case, Schrodinger's Equation for One Particle in a 1-Dimentional Box
Deep_kolmogorov 16 ⭐
Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning
Gerjoii 13 ⭐
Radar and DC resistivity 2.5D multi-physics inversion suite. Forward modeling, separate inversions, joint inversions.
Pde Vae Pytorch 11 ⭐
PDE-VAE: Variational Autoencoder for Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning