- S. McBane, Y. Choi, K. Willcox, Stress-constrained topology optimization of lattice-like structures using component-wise reduced order models, arXiv preprint, arXiv:2205.09629, 2022
- C.F. Jekel, D.M. Sterbentz, S. Aubry, Y. Choi, D.A. White, J.L. Belof, Using Conservation Laws to Infer Deep Learning Model Accuracy of Richtmyer-Meshkov Instabilities arXiv preprint, arXiv:2208.11477, 2022
- Q.A. Huhn, M.E. Tano, J.C. Ragusa, Y. Choi, Parametric dynamic mode decomposition for reduced order modeling, arXiv preprint, arXiv:2204.12006, 2022
- X. He, Y. Choi, W. Fries, J. Belof, J.S. Chen, gLaSDI: Parametric physics-informed greedy latent space dynamics identification, arXiv preprint, arXiv:2204.12005, 2022
- J.T. Lauzon, S.W. Cheung, Y. Shin, Y. Choi, D. M. Copeland, K. Huynh, S-OPT: a points selection algorithm for hyper-reduction in reduced order models, arXiv preprint, arXiv:2203.16494, 2022
- T. Kadeethum, D. O'Malley, Y. Choi, H.S. Viswanathan, N. Bouklas, and H. Yoon, Continuous conditional generative adversarial networks for data-driven solutions of poroelasticity with heterogeneous material properties, Computers & Geosciences, Volume 167, 105212, 2022
- W. Fries, X. He, Y. Choi, LaSDI: Parametric latent space dynamics identification, Computer Methods in Applied Mechanics and Engineering, volume 399, 115436, 2022, Also available as arXiv:2203.02076.
- T. Kadeethum, F. Ballarin, D. O'Malley, Y. Choi, N. Bouklas, H. Yoon, Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning, arXiv preprint, arXiv:2202.05460, 2022
- S.W Cheung, Y. Choi, D. Copeland, K. Huynh, Local Lagrangian reduced-order modeling for Rayleigh-Taylor instability by solution manifold decomposition, arXiv preprint, arXiv:2201.07335, 2022
- T. Kadeethum, F. Ballarin, Y. Choi, D. O'Malley, H. Yoon, N. Bouklas, Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: Comparison with linear subspace techniques, Advances in Water Resources, Volume 160, 104098, 2022
- Y. Kim, Y. Choi, D. Widemann, T. Zohdi, A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder, Journal of Computational Physics, 451, 110841, 2022. Also available as arXiv:2009.11990.
- T. Kadeethum, D. O'Malley, J.N. Fuhg, Y. Choi, J. Lee, H.S. Viswanathan, N. Bouklas, A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks, Nature Computational Science, 1, 819-829, 2021
- D. Copeland, S.W. Cheung, K. Huynh, Y. Choi, Reduced order models for Lagrangian hydrodynamics, Computer Methods in Applied Mechanics and Engineering, Volume 388, 114259, 2021. Also available as arXiv:2104.11404.
- C. Hoang, Y. Choi, K. Carlberg, Domain-decomposition least-squares Petrov-Galerkin (DD-LSPG) nonlinear model reduction, Computer Methods in Applied Mechanics and Engineering, Volume 384, 113997, 2021
- S. McBane, Y. Choi, Component-wise reduced order model lattice-type structure design, Computer Methods in Applied Mechanics and Engineering, 381, 113813, 2021
- Y. Kim, K.M. Wang, Y. Choi, Efficient space-time reduced order model for linear dynamical systems in Python using less than 120 lines of code, Mathematics, 9(14), 1690, 2021
- Y. Choi, P. Brown, W. Arrighi, R. Anderson, K. Huynh, Space-time reduced order model for large-scale linear dynamical systems with application to Boltzmann transport problems, Journal of Computational Physics, 424, 109845, 2021. Also available as arXiv:1910.01260.
- Y. Choi, D. Coombs, R. Anderson, SNS: A Solution-based nonlinear subspace method for time-dependent model order reduction, SIAM Journal on Scientific Computing, 42(2), A1116-A1147, 2020
- Y. Choi, G. Boncoraglio, S. Anderson, D. Amsallem, C. Farhat Gradient-based constrained optimization using a database of linear reduced order models, Journal of Computational Physics, 423, 109787, 2020. Also available as arXiv:1506.07849.
- Y. Kim, Y. Choi, D. Widemann, T. Zohdi, Efficient nonlinear manifold reduced order model Workshop on machine learning for engineering modeling, simulation and design @ NeurIPS 2020, 2020. Also available as arXiv:2011.07727
2019 and earlier
- Y. Choi, K. Carlberg, Space-time least-squares Petrov-Galerkin projection for nonlinear model reduction, SIAM Journal on Scientific Computing, 41(1), A26-A58, 2019
- Y. Choi, G. Oxberry, D. Whit, T. Kirchdoerfer, Accelerating design optimization using reduced order models, arXiv preprint, arXiv:1909.11320, 2019
- K. Carlberg, Y. Choi, S. Sargsyan, Conservative model reduction for finite-volume models, Journal of Computational Physics, 371, p280-314, 2018
- G. Oxberry, T. Kostova-Vassilevska, W. Arrighi, K. Chand, Limited-memory adaptive snapshot selection for proper orthogonal decomposition, International Journal of Numerical Methods in Engineering, 109(2), p198-217, 2016
- D. Amsallem, M. Zahr, Y. Choi, C. Farhat, Design optimization using hyper-reduced-order models, Structural and Multidisciplinary Optimization, 51, p919-940, 2015