gmsh meshing for pipes

Fast and simple fluid simulator in Julia

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

Pure Python/Numpy implementation of the Nelder-Mead algorithm.

Modernized Minpack: for solving nonlinear equations and nonlinear least squares problems

Accelerating Eulerian Fluid Simulation With Convolutional Networks

Python Implementation of Reinforcement Learning: An Introduction

This is a python implementation of NSGA-II algorithm. NSGA is a popular non-domination based genetic algorithm for multi-objective optimization. It is a very effective algorithm but has been genera…

Tutorials, assignments, and competitions for MIT Deep Learning related courses.

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

• Data preprocessing and Data base management using MySQL. • Coverage Analysis using SVR (Support Vector Regression), ANN (Artificial Neural Network) with Keras, Tensor Flow and Outlier Detection t…

A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.

⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 instead.

Solution of Backward-Facing step flow problem using 2D-incompressible Navier Stokes Solver, with OpenMP

Basic Computational Fluid Dynamics (CFD) schemes implemented in FORTRAN using Finite-Volume and Finite-Difference Methods. Sample simulations and figures are provided.

Code accompanying The Lattice Boltzmann Method: Principles and Practice

Errata for The Lattice Boltzmann Method: Principles and Practice

Code samples for my book "Neural Networks and Deep Learning"

2-D simulation of steady viscous laminar flow over a backward step

This code implements the Tensor Basis Neural Network (TBNN) as described in Ling et al. (Journal of Fluid Mechanics, 2016).

The development repository for the deal.II finite element library.