A solution, written in C, to the heat equation using Crank-Nicholson and finite differences.
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Updated
Jul 14, 2017 - C
A solution, written in C, to the heat equation using Crank-Nicholson and finite differences.
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.
Exercise solutions of the course : Introduction to Computational Physics offered at ETH Zurich
An easy-to-use, Python library designed to approximate solutions to partial differential equations using primarily, finite difference methods.
Finite ring matrix algebra benchmark models for ROSS.
Mathematical examples from many areas, including population growth, epidemiology, cell biology, and traffic modeling. The models take the form of ordinary differential equations (ODEs), difference (recurrence) equations, and partial differential equations (PDEs).
Spatiotemporal mathematical model of Cdc42-mediated cell polarisation consisting of a coupled system of reaction diffusion equations
Differential equation solving in Python and C/C++
Solving the Laplace Equation numerically using Finite Difference Method
Partial Differential Equations (PDEs) and its application in Image Restoration
Finite difference scheme for 1D advection diffusion equation with periodic BCs
Implementation of the paper "Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism" [AAAI-MLPS 2021]
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
Simian Process Oriented Conservative JIT PDES from LANL
A collection of scripts for math visualization
Physics-informed neural networks (PINNs)
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