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A library for solving differential equations using neural networks based on PyTorch, used by multiple research groups around the world, including at Harvard IACS.
This repository is to prepare for Machine Learning interviews.
Julia package for Gaussian quadrature
Must-read Papers on Physics-Informed Neural Networks.
A library of systems of partial differential equations, as defined with ModelingToolkit.jl in Julia
18.065/18.0651: Matrix Methods in Data Analysis, Signal Processing, and Machine Learning
Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.
High-level model-order reduction to automate the acceleration of large-scale simulations
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization
Learning in infinite dimension with neural operators.
🏔️Optimization on Riemannian Manifolds in Julia
MATLAB codes for physics-informed dynamic mode decomposition (piDMD)
Learning Green's functions of partial differential equations with deep learning.
Tutorials and information on the Julia language for MIT numerical-computation courses.
mitmath / 18337
Forked from SciML/SciMLBook18.337 - Parallel Computing and Scientific Machine Learning
Chemical equilibrium for electrolytes system in pure python.
Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods
Repository for the Universal Differential Equations for Scientific Machine Learning paper, describing a computational basis for high performance SciML
18.S096 - Applications of Scientific Machine Learning
Course 18.S191 at MIT, Fall 2022 - Introduction to computational thinking with Julia
A flexible framework for solving PDEs with modern spectral methods.
Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)
18.335 - Introduction to Numerical Methods course
18.336 - Fast Methods for Partial Differential and Integral Equations