PSFC Seminar: C. Rackaukas

Tuesday, April 23, 2024 at 12:00pm to 1:00pm

Building NW17, 218
175 ALBANY ST, Cambridge, MA 02139

Abstract: Scientific machine learning (SciML) is the practice of adding scientific structure to improve the predictions from machine learning. In this talk we will showcase and explain how SciML techniques such as universal differential equations (UDEs) make it possible to improve the prediction and extrapolation capabilities of machine learning on small data. We will show various ways that physical laws, prior chemical knowledge, and conservation laws can be incorporated into a general learning process in order to give better predictions out of the same data. We will end by discussing some of the ways the SciML techniques can improve general machine learning with methods that automatically optimize hyperparameters, showing how solvers for ordinary differential equations can be used to give neural architectures with optimal depth and fast infinite layer architectures.

Bio: 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 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.

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Plasma Science and Fusion Center
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