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 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.
Sessions
The Julia SciML ecosystem is a collection of hundreds of packages. Keeping the whole system up to date can be quite the task, with dependencies releasing breaking versions weekly and having to track down CI failures. Over the last year a multi-agent system was developed to help with a lot of the maintenance burden. The goal of this talk is to share the details of this system so that other Julia package ecosystems can iterate on the idea and adopt similar mechanisms.
Chaotic neural dynamics resist equation discovery because parameter sensitivity creates intractable optimization landscapes. Using the SciML ecosystem, we combine prediction-error methods with universal differential equations (PEM-UDE) and multiple shooting to tame chaos during learning. In spiking networks, we derive novel mean-field equations for sparse cortical connectivity that predict frequency shifts and synchrony patterns, validated by intracranial recordings.
There are hundreds of ODE solvers in DifferentialEquations.jl. Which is the best one for your problem? In this talk we go through the many classes and types of solvers and build a map to help you understand when to use various choices. We start by highlighting the basics, the core solvers that tend to do well for all problems, and then start to showcase more specialized methods and detail when they are likely to be improvements.