ORCID: here, Google Scholar: here
Youngsoo is a computational math scientist in CASC under Computing directorate at LLNL. His research focuses on developing efficient reduced order models for various physical simulations for time-sensitive decision-making multi-query problems, such as inverse problems, design optimization, and uncertainty quantification. His expertise include various scientific computing disciplines as indicated in "Research interests" below. Together with his collaborators, he has developed various powerful model order reduction techniques, such as machine learning-based nonlinear manifold and space–time reduced order models for nonlinear dynamical systems. He has also developed the component-wise reduced order model optimization algorithm, which enables fast and accurate computational modeling tool for lattice-structure design. He is currently leading data-driven surrogate modeling development team for various physical simulations, with whom he developed the open source codes, libROM and ROM for Laghos, Lagrangian hydrodynamics. He is also involved with quantum computing research. He has earned his undergraduate degree for Civil and Environmental Engineering from Cornell University and his PhD degree for Computational and Mathematical Engineering from Stanford University. He was a postdoc in Sandia National Laboratory and Stanford University prior to joining LLNL in 2017.
Colloquium at Center for Mathematics and Artificial Intelligence, April 16th, 2021
Analysis Junior Seminars at SISSA, March 19th, 2021
Cornell MAE Spring 2021 Colloquium, February 16, 2021.
Where are we with data-driven surrogate modeling for various physical simulations, January 19, 2021.
Component-wise reduced order model with its application to lattice-structure design, 14th WCCM and Eccomas Congress, January 11, 2020.
Efficient nonlinear manifold reduced order model, Machine Learning for Engineering Modeling, Simulation, and Design; Workshop at Neural Information Processing Systems, December 12, 2020.
PostDoc., Sandia National Laboratories, Livermore, CA, 2015 – 2017
PostDoc., Farhat Research Group, Stanford, CA, 2013 – 2015
Ph.D., Computational and mathematical engineering, Stanford University, 2013
B.S., Civil and environmental engineering, Cornell University, 2007
A.S., Engineering, Montgomery County Community College, 2004
A contributed talk at IOP Institute of Physics, PETER 2021, May 25-27th, 2021
A contributed talk at ECCOMAS Thematic Conference, Computational Sciences and AI in Industry, CSAI2021, June 7-9th, 2021
A contributed talk at WCSMO-14, June 13-18th, 2021
A contributed talk at mini-symposium on model order reduction ofr physical simulations, 16th U.S. National Congress on Computational Mechanics, July 25-29th, 2021
A contributed talk at mini-symposium (MS 4-9) on advances in reduced order modeling of solids, fluids and porous media, MMLDT-CSET 2021, September 26-29th, 2021
A contributed talk at WCCM-APCOM Yokohama 2022, July 31st - August 5th, 2022
A seminar talk at Technical University of Munich,May 6th, 2021
A seminar talk at Colloquium at Center for Mathematics and Artificial Intelligence, April 16th, 2021
A seminar talk at PMSL seminar, ExxonMobile, April 14th, 2021
A seminar talk at CSC seminar, Max Planck Institute, April 13th, 2021
A seminar talk at AJS - Analysis Junior Seminars, March 19th, 2021
"A fast and accurate neural network reduced order model for advection-dominated physical simulations" at SIAM Conference on Computational Science and Engineering(CSE21), March 1 – 5, 2021, virtual conference
A seminar talk at MEMS (Mechanical Engineering & Materials Science) seminar at Duke University, February 3rd, 2021
A seminar talk at Cornell Sibley School Seminar Series at Cornell University, February 16th, 2021
"Component-wise reduced order model lattice design" at 14th World Congress in Computational Mechanics and ECCOMAS Congress, January 11 – 15, 2021, virtual conference
"Efficient nonlinear manifold reduced order model" at Machine Learning for Engineering Modeling, Simulation, and Design, Workshop at Neural Information Processing Systems, December 12, 2020, virtual conference
"Space–time reduced order model for Boltzmann transport equation" at 26th International Conference on Transport Theory, September 22-27, 2019, Paris, France
"A practical space–time reduced order model for large-scale dynamical problems" at 15th U.S. National Congress on Computational Mechanics, July 28-August 1, 2019, Austin, Texas, USA
"Accelerating topology optimization process using reduced order models" at the World Congress of Structural and Multidisciplinary Optimization, May 20-24, 2019, Beijing, China
"Accelerating Training phase in time-dependnet nonlinear model order reduction" at SIAM Conference on Computational Science and Engineering, February 25 - March 1, 2019, Spokane, Washington, USA
"ST-GNAT and SNS: Model order reduction techniques for nonlinear dynamical systems." Linear Algebra and Optimization Seminar at Stanford University (October 11, 2018)
"ST-GNAT and SNS: Model order reduction techniques for time-dependent nonlinear system of equations." Applied Mathematics Seminar at UC Berkeley/Lawrence Berkeley Laboratory, California (Sept. 13th 2018).
"Reduced representation for accelerating stress-constrained topology optimization." 13th World Congress on Computational Mechanics, Marriot Marquis, New York City, NY (July 20th ~ 27th 2018).
"Tensor decompositions in reduced order models." Machine Learning Reading Group, LLNL, California (May 22th 2018).
"Tensor decompositions in reduced order models." SIAM Conference on Applied Linear Algebra, Hong Kong Baptist University (May 4th ~ 8th 2018).
"Space–time least-squares Petrov–Galerkin projection for nonlinear model reduction." 2017 West Coast ROM workshop at LBNL, California (Nov. 17th 2017).
"Space–time least-squares Petrov–Galerkin projection in nonlinear model reduction." PDE and Applied Math Seminar at UC Davis, California (Oct. 12th 2017).
"Gradient-based constrained optimization using a database of linear reduced-order models." LLNL Design Optimization Symposium (Sept. 22th 2017).
"Space–time least-squares Petrov–Galerkin projection for nonlinear model reduction." at the 14th U.S. National Congress for Computational Mechanics, Montreal, Canada (July 17th ~ 20th 2017).
"Space–time least-squares Petrov–Galerkin projection for nonlinear model reduction." Scientific computing and matrix computations seminar at UC Berkeley, California (April 12th 2017).
Copeland, D., Cheung, S.W., Huynh, K., & Choi, Y. (2021). Reduced order models for Lagrangian hydrodynamics. arXiv preprint, arXiv:2104.11404.
McBane, S. & Choi, Y. (2021). Component-wise reduced order model lattice-type structure design. Computer Methods in Applied Mechanics and Engineering, 381, 113813.
Choi, Y., Brown, P., Arrighi, B., Anderson, R., & Huynh, K. (2021). Space-time reduced order model for large-scale linear dynamical systems with application to Boltzmann transport problems. Journal of Computational Physics, Volume 424, 109845.
Kim, Y., Wang, K., & Choi, Y. (2020). Efficient space–time reduced order model for linear dynamical systems in Python using less than 120 lines of code. arXiv preprint, arXiv:2011.10648.
Kim, Y., Choi, Y., Widemann, D., & Zohdi, T. (2020). A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder. arXiv preprint, arXiv:2009.11990.
Anders, P., Garcia, F., Appelo, D., Guenther, S., Choi, Y., & Vogt, R. (2020). Quantum Physics without the Physics. arXiv preprint, arXiv:2012.03865.
Kim, Y., Choi, Y., Widemann, D., & Zohdi, T. (2020). Efficient nonlinear manifold reduced order model. 2020 Conference on Neural Information Processing Systems.
Hoang, C., Choi, Y., & Carlberg, K. (2020). Domain-decomposition least-squares Petrov-Galerkin (DD-LSPG) nonlinear model reduction. arXiv preprint, arXiv:2007.11835.
Choi, Y., Boncoraglio, G., Anderson, S., Amsallem, D., & Farhat, C. (2020). Gradient-based constrained optimization using a database of linear reduced-order models. Journal of Computational Physics, Volume 423, 109787
Choi, Y., Coombs, D., & Anderson, R. (2020). SNS: A Solution-based Nonlinear Subspace method for time-dependent nonlinear model order reduction. SIAM Journal of Scientific Computing, 42(2), A1116–A1146.
White, D., Choi, Y., & Kudo, J. (2020). A dual mesh method with adaptivity for stress constrained topology optimization. Structural and Multidisciplinary Optimization, 61:749–762
Choi, Y., Oxberry, G., White, D., & Kirchdoerfer, T. (2019). Accelerating topologyoptimization using reduced order models. World Congress of Structural and MultidisciplinaryOptimization
Choi, Y. & Carlberg, K. (2019). Space–time least-squares Petrov–Galerkin projection for nonlinear model reduction. SIAM Journal on Scientific Computing, 41(1):A26-A58
Carlberg, K., Choi, Y., & Sargsyan, S. (2018). Conservative model reduction for finite-volume models. Journal of Computational Physics, 371, 280-314.
Choi, Y., Farhat, C., Murray, W., & Saunders, M. (2015). A practical factorization of a Schur complement for PDE-constrained distributed optimal control. Journal of Scientific Computing, 65(2), 576-597.
Amsallem, D., Neumann, D., Choi, Y., & Farhat, C. (2015). Linearized Aeroelastic Computations in the Frequency Domain Based on Computational Fluid Dynamics. arXiv preprint arXiv:1506.07441.
Amsallem, D., Zahr, M., Choi, Y., & Farhat, C. (2015). Design optimization using hyper-reduced-order models. Structural and Multidisciplinary Optimization, 51(4), 919-940.
Choi, Y. (2012). Simultaneous analysis and design in PDE-constrained optimization (Doctoral dissertation, Stanford University).
Wein, L. M., Choi, Y., & Denuit, S. (2010). Analyzing Evacuation Versus Shelter-in-Place Strategies After a Terrorist Nuclear Detonation. Risk Analysis, 30(9), 1315-1327.