56 Repositories
Python hyperbolic-equations Libraries
Reproduces the results of the paper "Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations".
Finite basis physics-informed neural networks (FBPINNs) This repository reproduces the results of the paper Finite Basis Physics-Informed Neural Netwo
Hyperbolic Image Segmentation, CVPR 2022
Hyperbolic Image Segmentation, CVPR 2022 This is the implementation of paper Hyperbolic Image Segmentation (CVPR 2022). Repository structure assets :
Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable.
Diffrax Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. Diffrax is a JAX-based library providing numerical differe
Python wrapper and CLI utility to render LaTeX markup and equations as SVG using dvisvgm and svgo.
latex2svg Python wrapper and CLI utility to render LaTeX markup and equations as SVG using dvisvgm and svgo. Based on the original work by Tino Wagner
Using NumPy to solve the equations of fluid mechanics together with Finite Differences, explicit time stepping and Chorin's Projection methods
Computational Fluid Dynamics in Python Using NumPy to solve the equations of fluid mechanics 🌊 🌊 🌊 together with Finite Differences, explicit time
A functional and efficient python implementation of the 3D version of Maxwell's equations
py-maxwell-fdfd Solving Maxwell's equations via A python implementation of the 3D curl-curl E-field equations. This code contains additional work to e
Deeplearning project at The Technological University of Denmark (DTU) about Neural ODEs for finding dynamics in ordinary differential equations and real world time series data
Authors Marcus Lenler Garsdal, [email protected] Valdemar Søgaard, [email protected] Simon Moe Sørensen, [email protected] Introduction This repo contains the
Examples of how to create colorful, annotated equations in Latex using Tikz.
The file "eqn_annotate.tex" is the main latex file. This repository provides four examples of annotated equations: [example_prob.tex] A simple one ins
A sequence of Jupyter notebooks featuring the 12 Steps to Navier-Stokes
CFD Python Please cite as: Barba, Lorena A., and Forsyth, Gilbert F. (2018). CFD Python: the 12 steps to Navier-Stokes equations. Journal of Open Sour
Finite Element Analysis
FElupe - Finite Element Analysis FElupe is a Python 3.6+ finite element analysis package focussing on the formulation and numerical solution of nonlin
Glyph-graph - A simple, yet versatile, package for graphing equations on a 2-dimensional text canvas
Glyth Graph Revision for 0.01 A simple, yet versatile, package for graphing equations on a 2-dimensional text canvas List of contents: Brief Introduct
Pywonderland - A tour in the wonderland of math with python.
A Tour in the Wonderland of Math with Python A collection of python scripts for drawing beautiful figures and animating interesting algorithms in math
Finite difference solution of 2D Poisson equation. Can handle Dirichlet, Neumann and mixed boundary conditions.
Poisson-solver-2D Finite difference solution of 2D Poisson equation Current version can handle Dirichlet, Neumann, and mixed (combination of Dirichlet
Python library for ODE integration via Taylor's method and LLVM
heyoka.py Modern Taylor's method via just-in-time compilation Explore the docs » Report bug · Request feature · Discuss The heyókȟa [...] is a kind of
Visualizations of some specific solutions of different differential equations.
Diff_sims Visualizations of some specific solutions of different differential equations. Heat Equation in 1 Dimension (A very beautiful and elegant ex
Code for hyperboloid embeddings for knowledge graph entities
Implementation for the papers: Self-Supervised Hyperboloid Representations from Logical Queries over Knowledge Graphs, Nurendra Choudhary, Nikhil Rao,
A short term landscape evolution using a path sampling method to solve water and sediment flow continuity equations and model mass flows over complex topographies.
r.sim.terrain A short-term landscape evolution model that simulates topographic change for both steady state and dynamic flow regimes across a range o
Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed computing
Notice: Support for Python 3.6 will be dropped in v.0.2.1, please plan accordingly! Efficient and Scalable Physics-Informed Deep Learning Collocation-
PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks
PyDEns PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks. With PyDEns one can solve PD
Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
NeuralPDE NeuralPDE.jl is a solver package which consists of neural network solvers for partial differential equations using scientific machine learni
Python framework for Stochastic Differential Equations modeling
SDElearn: a Python package for SDE modeling This package implements functionalities for working with Stochastic Differential Equations models (SDEs fo
Multiwavelets-based operator model
Multiwavelet model for Operator maps Gaurav Gupta, Xiongye Xiao, and Paul Bogdan Multiwavelet-based Operator Learning for Differential Equations In Ne
Simulation and Parameter Estimation in Geophysics
Simulation and Parameter Estimation in Geophysics - A python package for simulation and gradient based parameter estimation in the context of geophysical applications.
"Graph Neural Controlled Differential Equations for Traffic Forecasting", AAAI 2022
Graph Neural Controlled Differential Equations for Traffic Forecasting Setup Python environment for STG-NCDE Install python environment $ conda env cr
Discontinuous Galerkin finite element method (DGFEM) for Maxwell Equations
DGFEM Maxwell Equations Discontinuous Galerkin finite element method (DGFEM) for Maxwell Equations. Work in progress. Currently, the 1D Maxwell equati
Hyperbolic Procrustes Analysis Using Riemannian Geometry
Hyperbolic Procrustes Analysis Using Riemannian Geometry The code in this repository creates the figures presented in this article: Please notice that
This thesis is mainly concerned with state-space methods for a class of deep Gaussian process (DGP) regression problems
Doctoral dissertation of Zheng Zhao This thesis is mainly concerned with state-space methods for a class of deep Gaussian process (DGP) regression pro
PyTorch code for Composing Partial Differential Equations with Physics-Aware Neural Networks
FInite volume Neural Network (FINN) This repository contains the PyTorch code for models, training, and testing, and Python code for data generation t
PyTorch implementation for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)
Score-Based Generative Modeling through Stochastic Differential Equations This repo contains a PyTorch implementation for the paper Score-Based Genera
It's a repo for Cramer's rule, which is some math crap or something idk
It's a repo for Cramer's rule, which is some math crap or something idk (just a joke, it's not crap; don't take that seriously, math teachers)
Hyperbolic Hierarchical Clustering.
Hyperbolic Hierarchical Clustering (HypHC) This code is the official PyTorch implementation of the NeurIPS 2020 paper: From Trees to Continuous Embedd
NDE: Climate Modeling with Neural Diffusion Equation, ICDM'21
Climate Modeling with Neural Diffusion Equation Introduction This is the repository of our accepted ICDM 2021 paper "Climate Modeling with Neural Diff
Analysis scripts for QG equations
qg-edgeofchaos Analysis scripts for QG equations FIle/Folder Structure eigensolvers.py - Spectral and finite-difference solvers for Rossby wave eigenf
Numerical methods for ordinary differential equations: Euler, Improved Euler, Runge-Kutta.
Numerical methods Numerical methods for ordinary differential equations are methods used to find numerical approximations to the solutions of ordinary
Deep learning library for solving differential equations and more
DeepXDE Voting on whether we should have a Slack channel for discussion. DeepXDE is a library for scientific machine learning. Use DeepXDE if you need
BioMASS - A Python Framework for Modeling and Analysis of Signaling Systems
Mathematical modeling is a powerful method for the analysis of complex biological systems. Although there are many researches devoted on produ
A Python framework for developing parallelized Computational Fluid Dynamics software to solve the hyperbolic 2D Euler equations on distributed, multi-block structured grids.
pyHype: Computational Fluid Dynamics in Python pyHype is a Python framework for developing parallelized Computational Fluid Dynamics software to solve
Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations
Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations This repo contains official code for the NeurIPS 2021 paper Imi
Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs
PhyCRNet Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs Paper link: [ArXiv] By: Pu Ren, Chengping Rao, Yang
[NeurIPS 2021] Galerkin Transformer: a linear attention without softmax
[NeurIPS 2021] Galerkin Transformer: linear attention without softmax Summary A non-numerical analyst oriented explanation on Toward Data Science abou
Control System Packer is a lightweight, low-level program to transform energy equations into the compact libraries for control systems.
Control System Packer is a lightweight, low-level program to transform energy equations into the compact libraries for control systems. Packer supports Python 🐍 , C 💻 and C++ 💻 libraries.
Curvlearn, a Tensorflow based non-Euclidean deep learning framework.
English | 简体中文 Why Non-Euclidean Geometry Considering these simple graph structures shown below. Nodes with same color has 2-hop distance whereas 1-ho
Leibniz is a python package which provide facilities to express learnable partial differential equations with PyTorch
Leibniz is a python package which provide facilities to express learnable partial differential equations with PyTorch
pix2tex: Using a ViT to convert images of equations into LaTeX code.
The goal of this project is to create a learning based system that takes an image of a math formula and returns corresponding LaTeX code.
Partial implementation of ODE-GAN technique from the paper Training Generative Adversarial Networks by Solving Ordinary Differential Equations
ODE GAN (Prototype) in PyTorch Partial implementation of ODE-GAN technique from the paper Training Generative Adversarial Networks by Solving Ordinary
PyTorch implementation for SDEdit: Image Synthesis and Editing with Stochastic Differential Equations
SDEdit: Image Synthesis and Editing with Stochastic Differential Equations Project | Paper | Colab PyTorch implementation of SDEdit: Image Synthesis a
PyTorch implementation HoroPCA: Hyperbolic Dimensionality Reduction via Horospherical Projections
HoroPCA This code is the official PyTorch implementation of the ICML 2021 paper: HoroPCA: Hyperbolic Dimensionality Reduction via Horospherical Projec
HyperLib: Deep learning in the Hyperbolic space
HyperLib: Deep learning in the Hyperbolic space Background This library implements common Neural Network components in the hypberbolic space (using th
Solve various integral equations using numerical methods in Python
Solve Volterra and Fredholm integral equations This Python package estimates Volterra and Fredholm integral equations using known techniques. Installa
Differentiable SDE solvers with GPU support and efficient sensitivity analysis.
PyTorch Implementation of Differentiable SDE Solvers This library provides stochastic differential equation (SDE) solvers with GPU support and efficie
Code for: Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space. Nicholas Monath, Manzil Zaheer, Daniel Silva, Andrew McCallum, Amr Ahmed. KDD 2019.
gHHC Code for: Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space. Nicholas Monath, Manzil Zaheer, D
Supplementary code for the paper "Meta-Solver for Neural Ordinary Differential Equations" https://arxiv.org/abs/2103.08561
Meta-Solver for Neural Ordinary Differential Equations Towards robust neural ODEs using parametrized solvers. Main idea Each Runge-Kutta (RK) solver w
Code for "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"
Infinitely Deep Bayesian Neural Networks with SDEs This library contains JAX and Pytorch implementations of neural ODEs and Bayesian layers for stocha
Python script for Linear, Non-Linear Convection, Burger’s & Poisson Equation in 1D & 2D, 1D Diffusion Equation using Standard Wall Function, 2D Heat Conduction Convection equation with Dirichlet & Neumann BC, full Navier-Stokes Equation coupled with Poisson equation for Cavity and Channel flow in 2D using Finite Difference Method & Finite Volume Method.
Navier-Stokes-numerical-solution-using-Python- Python script for Linear, Non-Linear Convection, Burger’s & Poisson Equation in 1D & 2D, 1D D
AnuGA for the simulation of the shallow water equation
ANUGA Contents ANUGA What is ANUGA? Installation Documentation and Help Mailing Lists Web sites Latest source code Bug reports Developer information L
Official code for Score-Based Generative Modeling through Stochastic Differential Equations
Score-Based Generative Modeling through Stochastic Differential Equations This repo contains the official implementation for the paper Score-Based Gen