mitmath / 18335
18.335 - Introduction to Numerical Methods course
☆499Updated 6 months ago
Related projects ⓘ
Alternatives and complementary repositories for 18335
- 18.330 Introduction to Numerical Analysis☆355Updated 9 months ago
- Tutorials and information on the Julia language for MIT numerical-computation courses.☆736Updated 2 months ago
- 18.303 - Linear PDEs course☆140Updated 11 months ago
- 18.S096 - Applications of Scientific Machine Learning☆306Updated 2 years ago
- 18.336 - Fast Methods for Partial Differential and Integral Equations☆180Updated 6 months ago
- MIT IAP short course: Matrix Calculus for Machine Learning and Beyond☆314Updated last month
- 18.337 - Parallel Computing and Scientific Machine Learning☆226Updated last year
- Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)☆1,859Updated last month
- Extra materials for *Fundamentals of Numerical Computation* by Driscoll and Braun.☆158Updated 2 years ago
- Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.☆720Updated 6 months ago
- Julia code for the book Numerical Linear Algebra☆114Updated last year
- ☆94Updated this week
- A template for textbooks in the same style as Algorithms for Optimization☆352Updated 5 months ago
- Core functions for the Julia (2nd) edition of the text Fundamentals of Numerical Computation, by Driscoll and Braun.☆98Updated last week
- Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learnin…☆871Updated this week
- Jupyter notebooks associated with the Algorithms for Optimization textbook☆419Updated 2 years ago
- Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem☆278Updated last week
- Materials for MIT 6.S083 / 18.S190: Computational thinking with Julia + application to the COVID-19 pandemic☆503Updated last year
- 18.065/18.0651: Matrix Methods in Data Analysis, Signal Processing, and Machine Learning☆141Updated 2 weeks ago
- 18.S096 three-week course at MIT☆259Updated last year
- Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax),…☆319Updated this week
- Grid-based approximation of partial differential equations in Julia☆713Updated this week
- Forward Mode Automatic Differentiation for Julia☆893Updated this week
- High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differ…☆558Updated this week
- 🏔️Manopt. jl – Optimization on Manifolds in Julia☆323Updated this week
- Manifolds.jl provides a library of manifolds aiming for an easy-to-use and fast implementation.☆376Updated this week
- Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization☆407Updated this week
- Taylor polynomial expansions in one and several independent variables.☆352Updated 3 weeks ago
- Repository for Common Ground C25☆94Updated this week
- Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated s…☆994Updated last week