Pinned Repositories
1806
18.06 course at MIT
18330
18.330 Introduction to Numerical Analysis
18335
18.335 - Introduction to Numerical Methods course
18337
18.337 - Parallel Computing and Scientific Machine Learning
18S096
18.S096 three-week course at MIT
18S096SciML
18.S096 - Applications of Scientific Machine Learning
6S083
Materials for MIT 6.S083 / 18.S190: Computational thinking with Julia + application to the COVID-19 pandemic
computational-thinking
Course 18.S191 at MIT, Fall 2022 - Introduction to computational thinking with Julia
julia-mit
Tutorials and information on the Julia language for MIT numerical-computation courses.
matrixcalc
MIT IAP short course: Matrix Calculus for Machine Learning and Beyond
MITMath's Repositories
mitmath/computational-thinking
Course 18.S191 at MIT, Fall 2022 - Introduction to computational thinking with Julia
mitmath/1806
18.06 course at MIT
mitmath/julia-mit
Tutorials and information on the Julia language for MIT numerical-computation courses.
mitmath/18335
18.335 - Introduction to Numerical Methods course
mitmath/18330
18.330 Introduction to Numerical Analysis
mitmath/matrixcalc
MIT IAP short course: Matrix Calculus for Machine Learning and Beyond
mitmath/18336
18.336 - Fast Methods for Partial Differential and Integral Equations
mitmath/18303
18.303 - Linear PDEs course
mitmath/18065
18.065/18.0651: Matrix Methods in Data Analysis, Signal Processing, and Machine Learning
mitmath/18369
18.369/8.315 - Mathematical Methods in Nanophotonics course
mitmath/18338
mitmath/JuliaComputation
Repository for Common Ground C25
mitmath/binder-env
Binder environments for MIT math courses
mitmath/18337projects
List of Student Projects for 18.337j
mitmath/18337sp2023-alexander_mercier-ExtendRandomVariables.jl
Julia package to extend RandomVariables.jl
mitmath/18337sp2023-alexander_wang-parallel-computing-sciml-rna-aptamer-design
mitmath/18337sp2023-amy_phung-GPUParticleFilter.jl
mitmath/18337sp2023-chenyue_lu-18337_Fa23_CL
mitmath/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.
mitmath/18337sp2023-kerri_lu-18337-anode-project
mitmath/18337sp2023-marc_davis-yieldtasks
mitmath/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.
mitmath/18337sp2023-nathan_stover__krystian_ganko-18.337-Project-SP2023
Project for Parallel Computing and Scientific Machine Learning course, Spring 2023.
mitmath/18337sp2023-riley_martell-mit_18337_SubpixelRegistration
Julia code for MIT 18.337 course's final project.
mitmath/18337sp2023-rodrigo_arrieta-NeuralODEProject
mitmath/18337sp2023-samuel_degnan-morgenstern-CahnHilliardSBM.jl
Fast Cahn Hilliard simulations in Custom Geometries using the Smoothed Boundary Method
mitmath/18337sp2023-vincent_fan___-adaptive-hermite-refinement
mitmath/18337sp2023-yadira_gaibor-JuliaFinalProject
mitmath/18337sp2023-zeyad_al_awwad-Julia-Ray-Tracing
My final project for MIT course 18.337 (Parallel Computing and Scientific Machine Learning)
mitmath/18337sp2023-zhang_wu-18337