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ChrisRackauckas/README.md

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Websites: Personal Website | Blog

SciML Organization Stats: SciML Stars

Chris is the VP of Modeling and Simulation at Julia Computing, the Director of Scientific Research at Pumas-AI, Co-PI of the Julia Lab at MIT, and the lead developer of the SciML Open Source Software Organization. He is the lead developer of the Pumas project and has received a top presentation award at every ACoP in the last 3 years for improving methods for uncertainty quantification, automated GPU acceleration of nonlinear mixed effects modeling (NLME), and machine learning assisted construction of NLME models with DeepNLME. For these achievements, Chris received the Emerging Scientist award from ISoP. For his work in mechanistic machine learning, his work is credited for the 15,000x acceleration of NASA Launch Services simulations and recently demonstrated a 60x-570x acceleration over Modelica tools in HVAC simulation, earning Chris the US Air Force Artificial Intelligence Accelerator Scientific Excellence Award.

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  1. SciML/DifferentialEquations.jl SciML/DifferentialEquations.jl Public

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

    Julia 2.9k 228

  2. SciML/SciMLBook SciML/SciMLBook Public

    Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)

    HTML 1.9k 335

  3. SciML/ModelingToolkit.jl SciML/ModelingToolkit.jl Public

    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…

    Julia 1.4k 209

  4. JuliaSymbolics/Symbolics.jl JuliaSymbolics/Symbolics.jl Public

    Symbolic programming for the next generation of numerical software

    Julia 1.4k 154

  5. SciML/NeuralPDE.jl SciML/NeuralPDE.jl Public

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

    Julia 996 200

  6. SciML/DiffEqFlux.jl SciML/DiffEqFlux.jl Public

    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

    Julia 871 157