About Parallel Computing and Scientific Machine Learning

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In Fall 2020 and Spring 2021, this was MIT's 18.337J/6.338J: Parallel Computing and Scientific Machine Learning course. Now these lectures and notes serve as a standalone book resource.

https://github.com/SciML/SciMLBook
Chris Rackauckas, Massachusetts Institute of Technology

Additional information on these topics can be found at:

https://sciml.ai/ and other Julia programming language sites
Many of these descriptions originated on https://www.stochasticlifestyle.com/

Mixing Differential Equations and Neural Networks for Physics-Informed Learning

In Fall 2020 and Spring 2021, this was MIT's 18.337J/6.338J: Parallel Computing and Scientific Machine Learning course. Now these lectures and notes serve as a standalone book resource. https://github.com/SciML/SciMLBook Chris Rackauckas, Massachusetts Institute of Technology Additional informati…

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Uncertainty Programming: Differentiable Programming Extended to Uncertainty Quantification

In Fall 2020 and Spring 2021, this was MIT's 18.337J/6.338J: Parallel Computing and Scientific Machine Learning course. Now these lectures and notes serve as a standalone book resource. https://github.com/SciML/SciMLBook Chris Rackauckas, Massachusetts Institute of Technology Additional informati…

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Global Sensitivity Analysis

In Fall 2020 and Spring 2021, this was MIT's 18.337J/6.338J: Parallel Computing and Scientific Machine Learning course. Now these lectures and notes serve as a standalone book resource. https://github.com/SciML/SciMLBook Chris Rackauckas, Massachusetts Institute of Technology Additional informati…

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