MITMath

julia-mit

Tutorials and information on the Julia language for MIT numerical-computation courses.

6S083

Materials for MIT 6.S083 / 18.S190: Computational thinking with Julia + application to the COVID-19 pandemic

18330

18.330 Introduction to Numerical Analysis

18S096SciML

18.S096 - Applications of Scientific Machine Learning

18S096

18.S096 three-week course at MIT

18337

18.337 - Parallel Computing and Scientific Machine Learning

18336

18.336 - Fast Methods for Partial Differential and Integral Equations

18369

18.369/8.315 - Mathematical Methods in Nanophotonics course

binder-env

Binder environments for MIT math courses

18.0002

18337sp2023-amin_heyrani_nobari-18337-Linakge-Project

Code for 18337 project by Amin Heyrani Nobari

18337sp2023-andreas_p__mentze-SciML-Project-18.337-Spring-2023-MIT

18337sp2023-axel_feldmann-hss-gpu

18337sp2023-devang_sehgal__anurag_vaidya-shapley_julia

Implement Shapley values for Global Sensitivity Analysis in Julia

18337sp2023-eric_m__stewart-pinnsforsolids

Physics-informed Neural Networks for solving example continuum mechanics problems, for MIT class 18.337.

18337sp2023-harvey_merton-18.337_project

18337sp2023-jackson_warner_burns-xtb-ts-screener

Screening 'likely-to-converge' Transition States partially optimized by XTB

18337sp2023-kota_kondo-18337

18337sp2023-oliver_xie-project

18337sp2023-raymond_lin-parallel-sdf

Parallel signed distance field (SDF) computation in Julia.

18337sp2023-ulrik_unneberg__henrik_tidemann_kaarbo-18.337-Project-LNN

Temporary template structure for development and structuring of the term project in 18.337

18337sp2023-utkarsh__prakitr_srisuma-DiffEqGPU.jl

GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem

18337sp2023-yassine_el_janati__georgios_efstathiadis-SDE-PINN-Solver

Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Stochastic Differential Equations for Scientific Machine Learning (SciML) accelerated simulation