MITMath

computational-thinking

Course 18.S191 at MIT, Fall 2022 - Introduction to computational thinking with Julia

1806

18.06 course at MIT

julia-mit

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

18335

18.335 - Introduction to Numerical Methods course

18330

18.330 Introduction to Numerical Analysis

matrixcalc

MIT IAP short course: Matrix Calculus for Machine Learning and Beyond

18336

18.336 - Fast Methods for Partial Differential and Integral Equations

18303

18.303 - Linear PDEs course

18065

18.065/18.0651: Matrix Methods in Data Analysis, Signal Processing, and Machine Learning

18369

18.369/8.315 - Mathematical Methods in Nanophotonics course

18338

JuliaComputation

Repository for Common Ground C25

binder-env

Binder environments for MIT math courses

18337projects

List of Student Projects for 18.337j

18337sp2023-alexander_mercier-ExtendRandomVariables.jl

Julia package to extend RandomVariables.jl

18337sp2023-alexander_wang-parallel-computing-sciml-rna-aptamer-design

18337sp2023-amy_phung-GPUParticleFilter.jl

18337sp2023-chenyue_lu-18337_Fa23_CL

18337sp2023-dariyan_khan-PotentialLearning-2.jl

An open source Julia library for active learning of interatomic potentials in atomistic simulations of materials. It incorporates elements of bayesian inference, machine learning, differentiable programming, software composability, and high-performance computing.

18337sp2023-kerri_lu-18337-anode-project

18337sp2023-marc_davis-yieldtasks

18337sp2023-minsik_cho-PotentialLearning.jl

An open source Julia library for active learning of interatomic potentials in atomistic simulations of materials. It incorporates elements of bayesian inference, machine learning, differentiable programming, software composability, and high-performance computing.

18337sp2023-nathan_stover__krystian_ganko-18.337-Project-SP2023

Project for Parallel Computing and Scientific Machine Learning course, Spring 2023.

18337sp2023-riley_martell-mit_18337_SubpixelRegistration

Julia code for MIT 18.337 course's final project.

18337sp2023-rodrigo_arrieta-NeuralODEProject

18337sp2023-samuel_degnan-morgenstern-CahnHilliardSBM.jl

Fast Cahn Hilliard simulations in Custom Geometries using the Smoothed Boundary Method

18337sp2023-vincent_fan___-adaptive-hermite-refinement

18337sp2023-yadira_gaibor-JuliaFinalProject

18337sp2023-zeyad_al_awwad-Julia-Ray-Tracing

My final project for MIT course 18.337 (Parallel Computing and Scientific Machine Learning)

18337sp2023-zhang_wu-18337