CSE Community Seminar
February 21, 2025, 12-1PM
Conference Room 45-432 in Building 45
Scientific Machine Learning for Kinetic Modeling and Uncertainty Quantification
Sili Deng
Associate Professor
Department of Mechanical Engineering, MIT
Abstract:
The combustion science is at the intersection of chemical kinetics and fluid dynamics, making this discipline physically rich and intellectually challenging. Applying tools developed in other disciplines to combustion research could provide us new perspectives and enable new discoveries. I will demonstrate our recent work on developing scientific machine learning tools for kinetic modeling and uncertainty quantification. Specifically, I will discuss the Chemical Reaction Neural Network approach to identify reaction pathways and simultaneously quantify kinetic parameters from data without any prior knowledge of the chemical system but bounded by fundamental physics, as well as an efficient kinetic uncertainty quantification framework for turbulent combustion simulations.
Feburary 21, 2025, CSE Community Seminar
Sili Deng
Associate Professor
Department of Mechanical Engineering, MIT