Chris Rackauckas
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.
Sessions
Many problems can be reduced down to solving f(x)=0, maybe even more than you think! Solving a stiff differential equation? Finding out where the ball hits the ground? Solving an inverse problem to find the parameters to fit a model? In this talk we'll showcase how SciML's NonlinearSolve.jl is a general system for solving nonlinear equations and demonstrate its ability to efficiently handle these kinds of problems with high stability and performance. We will focus on how compilers are being integrated into the numerical stack so that many of the things that were manual before, such as defining sparsity patterns, Jacobians, and adjoints, are all automated out-of-the-box making it greatly outperform purely numerical codes like SciPy or NLsolve.jl.