Scientific machine learning (SciML) methods are techniques which incorporate machine learning with mechanistic modeling. The purpose of this minisymposium is to share improved methods and applications of SciML to showcase the ever advancing ecosystem in Julia.
Examples of topics fit for this minisymposium include:
- New algorithms and software for physics-informed neural networks
- Tips, tricks, and techniques for improving convergence of universal differential equations
- Applications of fitting universal differential equations on real data and verification
- Methods which use neural networks with classical solvers in new ways
- Advancements in automatic differentiation for SciML applications
- Methods which use learning to accelerate numerical simulations (surrogates, metamodels, emulators, reduced order methods (ROMs))
- Advancements in compiler and optimization techniques to improve learning in SciML scenarios.
Dr. Chris Rackauckas is the VP of Modeling and Simulation at JuliaHub, 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. 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. See more at https://chrisrackauckas.com/. 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.