”partial-differential-equations“ 的搜索结果

Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations

Grid-based approximation of partial differential equations in Julia

Lecture material on numerical methods for partial differential equations.

Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations

Package for solving hyperbolic systems of partial differential equations

Python package for solving partial differential equations using finite differences.

#计算机科学#Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

Hidden physics models: Machine learning of nonlinear partial differential equations

Toolkit for solving partial differential equations

Code for "Learning data-driven discretizations for partial differential equations"

Solving High Dimensional Partial Differential Equations with Deep Neural Networks

Notes and examples on how to solve partial differential equations with numerical methods, using Python.

#计算机科学#[NeurIPS 2021] Galerkin Transformer: a linear attention without softmax for Partial Differential Equations

Survey of the packages of the Julia ecosystem for solving partial differential equations

Forward-Backward Stochastic Neural Networks: Deep Learning of High-dimensional Partial Differential Equations

Python package for numerical derivatives and partial differential equations in any number of dimensions.

Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equation...

#计算机科学#DAS: A deep adaptive sampling method for solving high-dimensional partial differential equations

PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks

Resources for "The Craft of Finite Difference Computing with Partial Differential Equations" by H. P. Langtangen

Solve forward and inverse problems related to partial differential equations using finite basis physics-informed neural networks (FBPINNs)