DDPS Seminar Talk: Generalizing Scientific Machine Learning and Differentiable Simulation Beyond Continuous models
November 12 2023 in Uncategorized | Tags: data-driven physics, ddps, physics-informed machine learning, piml, sciml | Author: Christopher Rackauckas
I�m pleased to share a talk I gave in the DDPS seminar series!
Data-driven Physical Simulations (DDPS) Seminar Series
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. However, the vast space of scientific simulation also includes models like jump diffusions, agent-based models, and more. Is SciML constrained to the simple continuous cases or is there a way to generalize to more advanced model forms? This talk will dive into the mathematical aspects of generalizing differentiable simulation to discuss cases like chaotic simulations, differentiating stochastic simulations like particle filters and agent-based models, and solving inverse � READ MORE
Generalized Physics-Informed Learning through Language-Wide Differentiable Programming (Video)
March 31 2020 in Differential Equations, Mathematics, Science, Scientific ML | Tags: physics-informed machine learning, pinn, scientific machine learning, scientific ml, sciml | Author: Christopher Rackauckas
Chris Rackauckas (MIT), �Generalized Physics-Informed Learning through Language-Wide Differentiable Programming�
Scientific computing is increasingly incorporating the advancements in machine learning to allow for data-driven physics-informed modeling approaches. However, re-targeting existing scientific computing workloads to machine learning frameworks is both costly and limiting, as scientific simulations tend to use the full feature set of a general purpose programming language. In this manuscript we develop an infrastructure for incorporating deep learning into existing scientific computing code through Differentiable Programming (∂P). We describe a ∂P system that is able to take gradients of full Julia programs, making Automatic Differentiation a first class language feature and compatibility with deep learning pervasive. Our system utilizes the one-language nature of Julia package development to augment the existing package ecosystem with deep learning, supporting almost all language � READ MORE