Junho Song’s Post

View profile for Junho Song, graphic

Dean, School of Transdisciplinary Innovations (SNUTI); Professor in Risk and Reliability Engineering, Department of Civil and Environmental Engineering (Joint Appt.) Seoul National University

The optimal design of a structural system poses significant challenges, such as the high dimensionality of random variable space and the cost of computational simulations repeated during optimization. In this new paper, co-authored by Dr. Jungho Kim at UC Berkeley (Kim Jungho; a former Ph.D. student of my research group at Seoul National University) and Prof. Ziqi Wang (Ziqi Wang) at UC Berkeley, we proposed to adaptively train a heteroscedastic Gaussian process based surrogate model described in a low dimensional active subspace for accurate and efficient optimization of structures. The proposed method was successfully demonstrated and tested by three examples: (1) nonlinear mathematical functions of up to 100 random variables, (2) a space truss structure described by 57 random variables, and (3) a steel lattice transmission tower subjected to seismic excitations described by 100 random variables. Congratulations, Dr. Kim!

[New paper] Active learning-based optimization of structures under stochastic excitations with first-passage probability constraints

[New paper] Active learning-based optimization of structures under stochastic excitations with first-passage probability constraints

http://systemreliability.wordpress.com

Jorge Egger Roa

Structural Engineer at Swanton Consulting Ltd

9mo

Congratulations professor! Is the time difference between 50 to 100 variables very high for this work? Took my attention because computational cost is one of the most important factors when we have many variables to obtain. I usually optimise around 10 variables of hysteretic models using MLE, and it can take and hour for a multilinear model, or a day for a Bouc Wen class model

To view or add a comment, sign in

Explore topics