Isaac-Somerville / Neural-Networks-for-Solving-Differential-Equations
Codebase for Master's dissertation in Mathematics at Durham University. Topic: applying neural networks to differential equations. Grade: 85/100.
☆11Updated last year
Alternatives and similar repositories for Neural-Networks-for-Solving-Differential-Equations:
Users that are interested in Neural-Networks-for-Solving-Differential-Equations are comparing it to the libraries listed below
- This codes calculates the dimensionalized POD and uses SINDy from the PySINDy python package to build a data-driven model for it. The cod…☆19Updated 3 years ago
- 🌌 Applications of Physics-Informed ML: A collection of notebooks from my Masters research, exploring how machine learning can solve scie…☆11Updated 5 months ago
- Coding numerical methods using ChatGPT: successes, failures, and challenges☆19Updated last year
- Implementation of a Physics Informed Neural Network (PINN) written in Tensorflow v2, which is capable of solving Partial Differential Equ…☆14Updated 3 years ago
- Code for "Nonlinear stochastic modeling with Langevin regression" J. L. Callaham, J.-C. Loiseau, G. Rigas, and S. L. Brunton☆25Updated 3 years ago
- Bayesian optimized physics-informed neural network for parameter estimation☆27Updated 5 months ago
- The unsupervised learning problem trains a diffeomorphic spatio-temporal grid, that registers the output sequence of the PDEs onto a non-…☆19Updated 2 years ago
- Methods and code for J. L. Callaham, J. N. Kutz, B. W. Brunton, and S. L. Brunton (2020)☆56Updated 4 years ago
- Deep reinforcement learning with OpenFOAM☆41Updated last year
- The Theory of Functional Connections: A functional interpolation method with applications in solving differential equations.☆39Updated last month
- Learning Green's functions of partial differential equations with deep learning.☆66Updated last year
- A step-by-step guide for surrogate optimization using Gaussian Process surrogate model☆32Updated 4 years ago
- Constructing linearizing transformations for reduced-order modeling of nonlinear dynamical systems☆10Updated 9 months ago
- Python scripts related to aerodynamic analysis and shape optimization☆19Updated 5 years ago
- ☆17Updated 6 months ago
- To address some of the failure modes in training of physics informed neural networks, a Lagrangian architecture is designed to conform to…☆48Updated 2 years ago
- Mathematica package for quadrature moment methods☆9Updated 2 years ago
- Variational Physic-informed Neural Operator (VINO) for Learning Partial Differential Equations☆13Updated 2 months ago
- Source code for the paper "Data-driven reduced-order models via regularised Operator Inference for a single-injector combustion process" …☆30Updated 2 years ago
- PINNs-JAX, Physics-informed Neural Networks (PINNs) implemented in JAX.☆47Updated 7 months ago
- ☆31Updated 2 years ago
- ☆13Updated 11 months ago
- Deep learning framework for model reduction of dynamical systems☆21Updated 4 years ago
- PySpectral is a Python package for solving the partial differential equation (PDE) of Burgers' equation in its deterministic and stochast…☆14Updated 2 years ago
- Python script solving the Burgers' equation (équation de Burgers) 1D by using FFT pseudo-spectral method.☆25Updated 3 years ago
- Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs☆31Updated 3 years ago
- Physics-informed neural networks (PINNs)☆12Updated 2 years ago
- Materials for my Structural and Multidisciplinary Design Optimization course☆42Updated 2 months ago
- Convolutional Solvers for Partial Differential Equations☆28Updated 4 years ago
- Repository from the paper https://arxiv.org/abs/1908.04127, to train Deep Reinforcement Learning in Fluid Mechanics Setup.☆64Updated 4 years ago