Lists (5)
Stars
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code
A sequence of Jupyter notebooks featuring the "12 Steps to Navier-Stokes" http://lorenabarba.com/
Classical Aerodynamics of potential flow using Python and Jupyter Notebooks
Learn deep learning with tensorflow2.0, keras and python through this comprehensive deep learning tutorial series. Learn deep learning from scratch. Deep learning series for beginners. Tensorflow t…
Investigating PINNs
Code accompanying my blog post: So, what is a physics-informed neural network?
Physics Informed Machine Learning Tutorials (Pytorch and Jax)
Python machine learning applications in image processing, recommender system, matrix completion, netflix problem and algorithm implementations including Co-clustering, Funk SVD, SVD++, Non-negative…
Materials for an online-course - "Practical XGBoost in Python"
Code for "Neural Networks for Topology Optimization"
Yet another black-box optimization library for Python
PINN (Physics-Informed Neural Networks) on Navier-Stokes Equations
A collection of small Python projects.
Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning
Automatically transform all categorical, date-time, NLP variables to numeric in a single line of code for any data set any size.
Material for the hands-on tutorial on Graph Deep Learning held at the Alan Turing Institute
SAASBO: a package for high-dimensional bayesian optimization
Deep Learning and Finite Element Method for Physical Systems Modeling
Bayesian Deep Learning and Deep Reinforcement Learning for Object Shape Error Response and Correction of Manufacturing Systems
Vortex Lattice Method library written in Python
Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO
BeamBending: a teaching aid for 1-D shear-force and bending-moment diagrams
A Physics-Informed Neural Network for solving Burgers' equation.
Sample codes of CNN-SINDy based reduced-order modeling for fluid flows by Fukami et al., JFM 2021.