9 Lessons
1 Students
A modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics .
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What You'll Learn
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Master Julia SciML tools for building scalable, parallelized scientific machine learning models in Julia environments
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Apply Julia's computer algebra system for symbolic computation and integrated modeling in scientific research
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Implement automatic parallelization techniques to optimize large-scale scientific machine learning workflows in Julia
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Utilize Julia SciML frameworks to enhance accuracy and efficiency in scientific data modeling and simulation
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Develop custom Julia scripts for symbolic mathematics and automated scientific machine learning processes
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Analyze complex scientific datasets using Julia's integrated symbolics and parallel computing capabilities
Course Content
9 Lessons • 2 h 11 m total
The Julia SciML Ecosystem Scientific Machine Learning as a Software Problem Chris Rackauckas
Modeling Spacecraft Separation Dynamics in Julia Jonathan Diegelman
Differentiable Programming for Quantum Control with SciML Frank Schäfer
Accelerating Scientific Machine Learning with Automatic Differentiable Surrogates Ludovico Bessi
Automated Solution of Symbolic Partial Differential Equations via Physics Informed Neural Networks
Modeling Biochemical Systems with Catalyst jl Samuel Isaacson
Automatic Numerical Bifurcation Analysis with Applications to Neurosciences with BifurcationKit
Mixed Feedbacks and Noise How Bacteria Modulate their Stress Response Torkel Loman
Simplifying Delays in Systems Biology | Julia SciML | SIAM CSE 2021 | Niklas Korsbo
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Julia SciML Symposium Course Description
A modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics .
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