Workshop: Uncertainty Management and Machine Learning in Engineering Applications
Date: November 16-17, 2020
Venue: Virtual event
Workshop Outline
Many high-consequence applications require various levels of risk management to ensure that project objectives are met. In such applications, the accurate quantification and management of risk often depend on large-scale computational models of the underlying physical processes-the outputs of which are plagued with uncertainties. These uncertainties arise from various sources including unverifiable or inaccurate model assumptions, unknown model parameters, estimated parameters using noisy or corrupt data, and underlying stochastic variability. To further complicate matters, the accurate quantification of statistical rare events for risk management typically requires numerous simulations of these models, which is computationally infeasible for many large-scale applications. Consequently, machine learning is likely to play a pivotal role in the risk management of these applications. During this workshop, we will investigate the interplay between machine learning, risk management and large engineering applications with talks from experts in these areas. Our goal is to facilitate conversations and to foster collaborations between experts in these traditionally disparate fields.
List of Speakers, Schedule, Abstracts and Presentations
List of SpeakersConference Schedule
Abstracts
Presentation Recordings
Organizers
Stan Uryasev, Pawel Polak, and Kevin Maritato Stony Brook University
Drew P. Kouri, Sandia National Laboratories
Contact Information
Mr. Kevin Maritato
kevin.maritato@stonybrook.edu
Prof. Stan Uryasev
Applied Mathematics and Statistics
Stony Brook University
stanislav.uryasev@stonybrook.edu
Cell Phone: (352) 213-3457
Dr. Drew P. Kouri
Optimization & Uncertainty Quantification
Sandia National Laboratories
dpkouri@sandia.gov