Announcement Detail
Monday, March 11, 2024
1:00 PM CST
USACM UQ Virtual Seminar
Extracting a surrogate model from results of dimensionality reduction in forward uncertainty quantification
Speaker
Ziqi Wang, University of California, Berkeley
Abstract
In this talk, I will present some preliminary results on extracting a surrogate model from the outcomes of dimensionality reduction. The hypothesis is that the high-dimensional input augmented by the output of a computational model may admit a low-dimensional representation. Subsequently, performing dimensionality reduction in the input-output space is akin to constructing a surrogate model. The final product of the proposed method is a stochastic simulator that propagates a deterministic input into a stochastic output. This preserves the convenience of the sequential "dimensionality reduction + Gaussian process regression" approach while overcoming some of its limitations.
Biography
Ziqi Wang is an assistant professor in the department of civil and environmental engineering at UC Berkeley. His research focuses on analyzing and understanding the reliability, risk, and resilience of structures and critical infrastructures under hazards. He is interested in computational methods of structural reliability and uncertainty quantification, focusing on interpretable probabilistic analysis methods leveraging domain/problem-specific knowledge. He also develops probabilistic methods to analyze the regional impact of hazards by adapting theories/models from reliability, uncertainty quantification, and statistical physics.