mitmath / 18337
18.337 - Parallel Computing and Scientific Machine Learning
☆235Updated last year
Alternatives and similar repositories for 18337:
Users that are interested in 18337 are comparing it to the libraries listed below
- 18.S096 - Applications of Scientific Machine Learning☆310Updated 2 years ago
- MIT IAP short course: Matrix Calculus for Machine Learning and Beyond☆387Updated last month
- 18.336 - Fast Methods for Partial Differential and Integral Equations☆185Updated 10 months ago
- GPU Programming with Julia - course at the Swiss National Supercomputing Centre (CSCS), ETH Zurich☆252Updated 2 years ago
- Core functions for the Julia (2nd) edition of the text Fundamentals of Numerical Computation, by Driscoll and Braun.☆102Updated 4 months ago
- 18.335 - Introduction to Numerical Methods course☆515Updated this week
- Repository for the Universal Differential Equations for Scientific Machine Learning paper, describing a computational basis for high perf…☆222Updated 2 years ago
- Survey of the packages of the Julia ecosystem for solving partial differential equations☆271Updated 2 months ago
- Repository for Common Ground C25☆98Updated 3 months ago
- 18.303 - Linear PDEs course☆141Updated last year
- Surrogate modeling and optimization for scientific machine learning (SciML)☆341Updated last week
- 18.330 Introduction to Numerical Analysis☆365Updated last year
- Julia code for the book Numerical Linear Algebra☆119Updated 2 years ago
- Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization☆411Updated last month
- Automatic Finite Difference PDE solving with Julia SciML☆171Updated 2 weeks ago
- Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax),…☆324Updated this week
- Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)☆1,877Updated last month
- DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia☆271Updated 5 months ago
- A short course on Julia and open-source software development☆309Updated 2 years ago
- Julia Programming for Machine Learning course at TU Berlin☆244Updated 4 months ago
- A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, …☆344Updated this week
- ETH course - Solving PDEs in parallel on GPUs☆127Updated 3 months ago
- Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated s…☆1,050Updated last week
- Package for writing high-level code for parallel high-performance stencil computations that can be deployed on both GPUs and CPUs☆336Updated 3 months ago
- Solving differential equations in parallel on GPUs - JuliaCon 2021 workshop☆94Updated last year
- Solution of nonlinear multiphysics partial differential equation systems using the Voronoi finite volume method☆238Updated this week
- Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.☆729Updated 10 months ago
- Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem☆285Updated last month
- ☆95Updated 2 weeks ago
- Linear operators for discretizations of differential equations and scientific machine learning (SciML)☆282Updated last year