Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equat…

An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning a…

Symbolic programming for the next generation of numerical software

Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

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

Arrays with arbitrarily nested named components.

Clapeyron provides a framework for the development and use of fluid-thermodynamic models, including SAFT, cubic, activity, multi-parameter, and COSMO-SAC.

Global documentation for the Julia SciML Scientific Machine Learning Organization

SPECTRE: Supersonic Phase-change Ejector Cycle for Thermal Refrigeration Efficiency. This projects computationally models various refrigeration cycles and compares them to a novel refrigeration cyc…