I am a postdoctoral scholar working with Professor Boris Kramer in the Department of Mechanical and Aerospace Engineering at UC San Diego. I am interested in developing uncertainty quantification and surrogate modeling methods for large-scale engineering systems.
I completed my Ph.D. in Civil Engineering at Vanderbilt University, where I was advised by Professor Sankaran Mahadevan. In my Ph.D., I worked on prediction uncertainty quantification methodology development for high-dimensional problems. I obtained my M.S.Eng. in Civil Engineering from University of Michigan, Ann Arbor, and B.E. in Civil Engineering from China University of Mining and Technology, Beijing.
Outside of my research, I enjoy going to ice hockey and baseball games with friends. Saturday Night in SMASHVILLE is my favorite.
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Selected Publications (view all )
Active learning for adaptive surrogate model improvement in high-dimensional problems
Yulin Guo, Paromita Nath, Sankaran Mahadevan, Paul Witherell
Structural and Multidisciplinary Optimization Journal
[Paper (open access)] [Abstract] [BibTex]
Abstract
This paper investigates a novel approach to efficiently construct and improve surrogate models in problems with high-dimensional input and output. The proposed method is demonstrated for the numerical simulation of an additive manufacturing part, with a high-dimensional field output quantity of interest (residual stress) in the component that has spatial variability due to the stochastic nature of multiple input variables (including process variables and material properties).
BibTex
@article{Active_learning_adpative_surrogate_high_dimension,
title={Active learning for adaptive surrogate model improvement in high-dimensional problems},
author={Guo, Yulin and Nath, Paromita and Mahadevan, Sankaran and Witherell, Paul},
journal={Structural and Multidisciplinary Optimization},
volume={67},
number={7},
pages={122},
year={2024},
publisher={Springer},
doi={10.1007/s00158-024-03816-9}
}
Digital twin approach for damage-tolerant mission planning under uncertainty
Pranav Karve, Yulin Guo, Berkcan Kapusuzoglu, Sankaran Mahadevan, Mulugeta A. Haile
Engineering Fracture Mechanics Journal
Abstract
In this article, we develop a methodology for intelligent mission planning using the digital twin approach, with the objective of performing the required work while meeting the damage tolerance requirement. The proposed approach has three components: damage diagnosis, damage prognosis, and mission optimization. All three components are affected by uncertainty regarding system properties, operational parameters, loading and environment, as well as uncertainties in sensor data and prediction models.
BibTex
@article{DT_damage_tolerant_planning_uncertainty,
title={Digital twin approach for damage-tolerant mission planning under uncertainty},
author={Karve, Pranav M and Guo, Yulin and Kapusuzoglu, Berkcan and Mahadevan, Sankaran and Haile, Mulugeta A},
journal={Engineering Fracture Mechanics},
volume={225},
pages={106766},
year={2020},
publisher={Elsevier},
doi={10.1016/j.engfracmech.2019.106766}
}