Julia kernel for Jupyter

Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.

Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.

Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R

Import code from IJulia Jupyter notebooks into Julia programs

MADS: Model Analysis & Decision Support

Global glacier model using Universal Differential Equations for climate-glacier interactions

Automated Bayesian model discovery for time series data

Examples of how to use the modeling capabilities developed under the Scalable Integrated Infrastructure Planning Initiative at NREL.

Spectra.jl aims at helping treatment of spectral (Raman, Infrared, XAS, NMR) data under the Julia language

Plots made Simple 📉

Quantum Optimal Control with Direct Collocation

Geothermal Cloud for Machine Learning

A convenience meta-package for quantum optimal control using the Pade Integrator COllocation (PICO) method

Relational piecewise-linear overapproximations of multi-dimensional functions

A Julia package that implements a category of reaction (transportation) network-type dynamical systems.

Nonnegative Matrix Factorization + k-means clustering and physics constraints for Unsupervised and Physics-Informed Machine Learning

Develop tools for ensemble Kalman filter

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Nonnegative Tensor Factorization + k-means clustering and physics constraints for Unsupervised and Physics-Informed Machine Learning

TensorNetworkCodes is a Julia library developed to support the following research: https://arxiv.org/abs/2109.11996

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A Julia library for fitting DDMs to pulse-based evidence accumulations task data

A simple package to automatically format Jupyter Notebook and Jupyter Lab cells using JuliaFormatter.

Bitemporal data management for prototypical life insurance data model

Enable passing of arguments to Julia Jupyter Notebooks from the command line.

StableSpectralElements.jl: Provably stable discontinuous spectral-element methods for conservation laws

Univariate density estimation via Bernstein polynomials; able to model explicit combinatorial shape constraints.

Physics-informed Machine Learning

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Invariant Causal Prediction in pure Julia

Helps manage computational experiments in Julia Jupyter notebooks

A macro to play a bell after running a command in a Jupyter notebook.

CanDecomp (CP) Tensor Decomposition

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Makie visualization module for TopOpt.jl

A simple fusion systems code for comparing pulsed and steady-state tokamaks

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Unsupervised Machine Learning: Nonnegative Tensor Networks + k-means clustering

Iterates Literate.jl