Chris Rackauckas
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 received a top presentation award at every ACoP from 2019-2021 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.
Session
Optimal control is a ubiquitous problem spanning applications of aerospace engineering to chemical processes. Because of that, there are as many softwares for solving optimal control problems as there are applications and domains. Is it possible to unify all optimal control software under one interface? Well that's what SciML does, and we've now entered this domain. In this talk we will demonstrate how ModelingToolkit can be used as a frontend to optimal control that is able to target many of the backend solver methods used throughout various domains. We will also showcase new solvers, built from adaptive BVP solvers, which greatly outperform the standard NLP formulation.