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Course 18.S191 at MIT, Fall 2022 - Introduction to computational thinking with Julia
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
Distributed High-Performance Symbolic Regression in Julia
Simple package for literate programming in Julia
This repository contains fundamental codes related to CFD that can be included in any graduate level CFD coursework.
DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia
An interactional aerodynamics and acoustics solver for multirotor aircraft and wind energy
The JuliaFEM software library is a framework that allows for the distributed processing of large Finite Element Models across clusters of computers using simple programming models. It is designed t…
A common interface for quadrature and numerical integration for the SciML scientific machine learning organization
Reservoir computing utilities for scientific machine learning (SciML)
Toolbox for gradient-based and derivative-free non-convex constrained optimization with continuous and/or discrete variables.
Fast uncertainty quantification for scientific machine learning (SciML) and differential equations
Efficient, Accurate, and Streamlined Training of Physics-Informed Neural Networks
A library for building differential equations arising from physical problems for physics-informed and scientific machine learning (SciML)
Workshop materials for training in scientific computing and scientific machine learning
Solvers for problems involving unsteady fluid flow
This is the source code of the Neural Partial Differential Equations for Chaotic Processes Paper.
Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components
Physics-informed nonlinear autoregression
SNF-ROM: Projection-based nonlinear reduced order modeling with smooth neural fields