mitmath / 18335Links
18.335 - Introduction to Numerical Methods course
☆544Updated 3 months ago
Alternatives and similar repositories for 18335
Users that are interested in 18335 are comparing it to the libraries listed below
Sorting:
- 18.330 Introduction to Numerical Analysis☆372Updated last year
- Tutorials and information on the Julia language for MIT numerical-computation courses.☆777Updated 6 months ago
- 18.S096 - Applications of Scientific Machine Learning☆312Updated 3 years ago
- 18.303 - Linear PDEs course☆145Updated last year
- 18.336 - Fast Methods for Partial Differential and Integral Equations☆186Updated last year
- 18.337 - Parallel Computing and Scientific Machine Learning☆241Updated 2 years ago
- Jupyter notebooks associated with the Algorithms for Optimization textbook☆467Updated 3 years ago
- Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)☆1,920Updated last week
- Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.☆733Updated this week
- Extra materials for *Fundamentals of Numerical Computation* by Driscoll and Braun.☆166Updated 2 months ago
- MIT IAP short course: Matrix Calculus for Machine Learning and Beyond☆520Updated 6 months ago
- Julia code for the book Numerical Linear Algebra☆126Updated 2 years ago
- Harvard Applied Math 205: Code Examples☆88Updated 3 years ago
- ☆103Updated last month
- Materials for MIT 6.S083 / 18.S190: Computational thinking with Julia + application to the COVID-19 pandemic☆509Updated 2 years ago
- Important concepts in numerical linear algebra and related areas☆774Updated last year
- Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learnin…☆893Updated this week
- A template for textbooks in the same style as Algorithms for Optimization☆369Updated last year
- Core functions for the Julia (2nd) edition of the text Fundamentals of Numerical Computation, by Driscoll and Braun.☆108Updated 8 months ago
- 18.065/18.0651: Matrix Methods in Data Analysis, Signal Processing, and Machine Learning☆171Updated 9 months ago
- Nonlinear Dynamics: A concise introduction interlaced with code☆242Updated last month
- 18.S096 three-week course at MIT☆265Updated 2 years ago
- Forward Mode Automatic Differentiation for Julia☆949Updated last week
- Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem☆290Updated this week
- Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem☆305Updated this week
- Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax),…☆328Updated this week
- Repository for the Universal Differential Equations for Scientific Machine Learning paper, describing a computational basis for high perf…☆228Updated 2 years ago
- 🏔️Optimization on Riemannian Manifolds in Julia☆372Updated this week
- Taylor polynomial expansions in one and several independent variables.☆359Updated 2 months ago
- Source code for lecture notes☆149Updated last year