Lists (3)
Stars
Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equat…
Course 18.S191 at MIT, Fall 2022 - Introduction to computational thinking with Julia
Bayesian inference with probabilistic programming.
Concise and beautiful algorithms written in Julia
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
Solve and estimate Dynamic Stochastic General Equilibrium models (including the New York Fed DSGE)
Grid-based approximation of partial differential equations in Julia
Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable i…
An extensible framework for geospatial data science and geostatistical modeling fully written in Julia
Types with default field values, keyword constructors and (un-)pack macros
Efficient implementation of struct arrays in Julia
Econometrics lecture notes with examples using the Julia language
Surrogate modeling and optimization for scientific machine learning (SciML)
Julia solvers for systems of nonlinear equations and mixed complementarity problems
Julia code for the book Reinforcement Learning An Introduction
Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem
An interface to various automatic differentiation backends in Julia.
DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia
A workshop on writing good scientific/research code: reproducible, re-traceable, readable, extendable, and well-documented.
Julia for Machine Learning course at TU Berlin
Fast Estimation of Linear Models with IV and High Dimensional Categorical Variables
Codes for the book "Julia Programming for Operations Research"