Keynote: Scientific Machine Learning Through Symbolic Numerics | Chris Rackauckas | JuliaCon 2023

Описание: Dr. Rackauckas is a Research Affiliate and Co-PI of the Julia Lab at the Massachusetts Institute of Technology, VP of Modeling and Simulation at JuliaHub and Creator / Lead Developer of JuliaSim. He's also the Director of Scientific Research at Pumas-AI and Creator / Lead Developer of Pumas, and Lead Developer of the SciML Open Source Software Organization.

Dr. Rackauckas's research and software is focused on Scientific Machine Learning (SciML): the integration of domain models with artificial intelligence techniques like machine learning. By utilizing the structured scientific (differential equation) models together with the unstructured data-driven models of machine learning, our simulators can be accelerated, our science can better approximate the true systems, all while enjoying the robustness and explainability of mechanistic dynamical models.

Abstract:
The combination of scientific models into deep learning structures, commonly referred to as scientific machine learning (SciML), has made great strides in the last few years in incorporating models such as ODEs and PDEs into deep learning through differentiable simulation. Such SciML methods have been gaining steam due to accelerating the development of high-fidelity models for improving industrial simulation and design. However, many of the methods from the machine learning world lack the robustness required for scaling to industrial tasks. What needs to change about SciML in order to allow for methods which can guarantee accuracy and quantify uncertainty? In this talk we will go through the numerics of the robustness in building and training SciML models. Numerical robustness of algorithms for handling neural networks with stiff dynamics, continuous machine learning methods with certifiably globally-optimal training, alternative loss functions to mitigating local minima, integration of Bayesian estimation with model discovery, and tools for validating the correctness of surrogate models will be discussed to demonstrate a next generation of SciML methods for industrial use. In particular, it will be shown how symbolic-numerics is integrating the compiler into the modeling process as a method to improve numerical robustness, blurring the lines between computer science and numerical analysis. Demonstrations of these methods in applications such as two-phase flow HVAC systems, modeling of sensors in Formula One cars, and lithium-ion battery packs will be used to showcase the improved robustness of these approaches over standard (scientific) machine learning.

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