The Use and Practice of Scientific Machine Learning

Scientific machine learning (SciML) methods allow for the automatic discovery of mechanistic models by infusing neural network training into the simulation process. In this talk we will start by showcasing some of the ways that SciML is being used, from discovery of extrapolatory epidemic models to nonlinear mixed effects models in pharmacology. From there, we will discuss some of the increasingly advanced computational techniques behind the training process, focusing on the numerical issues involved in handling differentiation of highly stiff and chaotic systems. The viewers will leave with an understanding of how compiler techniques are being infused into the simulation stack to increasingly automate the process of developing mechanistic models.

Benchmarks behind this talk can be found at the SciML Benchmarks.

Bio:

Chris is an Applied Mathematics Instructor at MIT and the lead developer of the SciML Open Source Software Organization, which includes DifferentialEquations.jl solver suite along with hundreds of state-of-the-art packages for mixing machine learning into mechanistic modeling. Chris’ work on high performance differential equation solving is the engine accelerating many applications from the MIT-CalTech CLiMA climate modeling initiative to the SIAM Dynamical Systems award winning DynamicalSystems.jl toolbox. As the Director of Scientific Research at Pumas-AI, Chris is the lead developer of Pumas, where he 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, the highest early career award in pharmacometrics. As the Director of Modeling and Simulation at Julia Computing, Chris is the lead developer of JuliaSim, where the 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.