#
hjlFrist
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The Theory of Functional Connections: A functional interpolation method with applications in solving differential equations.
PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks
Runge-Kutta adaptive-step solvers for nonlinear PDEs. Solvers include both exponential time differencing and integrating factor methods.
Deep renormalized Mori-Zwanzig (DrMZ) Julia package.
Physics Informed Neural Network (PINN) for the wave equation.
Numerical methods for solving Fractional Ordinary Differential Equation(FODE)
Python package containing the tools necessary for radial basis function (RBF) applications
simulate by solving the phase-field equations using a centered finite difference method（or FEM), and the video of matlab lesson
Numerical schemes for the Cahn-Hilliard equation.
A two-dimensional multiphase solver based on incompressible Navier-Stokes and conservative Allen-Cahn equations
High performance computational platform in Python for the spectral Galerkin method
Three Legendre-Galerkin Spectral Methods for Solving Normal Modes of Underwater Acoustic Propagation
An Efficient, Scalable and Optimized Python Framework for Deep Forest (2021.2.1)
Code for our paper "Hamiltonian Neural Networks"
Tools for computation with positive definite kernels (RBFs) in approximation, optimization and PDEs
source code of learning to discretize solving 1d scalar conservation laws via deep reinforcement learning
Codes for "Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View"
Group project for Deep Learning: Algorithms and Applications in Peking University 2018 Spring. This is a brief survey, discussion and implementation for deep Ritz method.
A demo for my DeepRitz method, especially for high contrast problem.
Numerical Methods for Partial Differential Equations (MATH F422 - BITS Pilani)
有限元方法的 matlab 实现，为了追求清晰通用，将会大大牺牲程序的效率。
Building blocks for spectral Galerkin method with Legendre polynomials
This repo explains the workings of Neural ODEs and how to use them effectively
