# Publications

#### 2024

- C. Bonneville, Y. Choi, D. Ghosh, J.L. Belof, GPLaSDI: Gaussian Process-based Interpretable Latent Space Dynamics Identification through Deep Autoencoder,
*Computer Methods in Applied Mechanics and Engineering*, 418, 116535,**2024**

#### 2023

- Y. Kim, Y. Choi, B. Yoo, Gappy data reconstruction using unsupervised learning for digital twin,
*arXiv preprint*, arXiv:2312.07902**2023** - S.W. Chung, Y. Choi, P. Roy, T. Moore, T. Roy, T.Y. Lin, D.T. Nguyen, C. Hahn, E.B. Duoss, S.E., Baker, Train small, model big: Scalable physics simulations via reduced order modeling and domain decomposition,
*arXiv preprint*, arXiv:2401.10245**2023** - C. Bonneville, Y. Choi, D. Ghosh, J.L. Belof Data-driven autoencoder numerical solver with uncertainty quantification for fast physical simulations, Machine Learning and the Physical Sciences Workshop,
*NeurIPS*,**2023** - A.N. Diaz, Y. Choi, M. Heinkenschloss Nonlinear-manifold reduced order models with domain decomposition, Machine Learning and the Physical Sciences Workshop,
*NeurIPS*,**2023** - S.W. Suh, S.W. Chung, P.T. Bremer, Y. Choi Accelerating flow simulations using online dynamic mode decomposition, Machine Learning and the Physical Sciences Workshop,
*NeurIPS*,**2023** - A.L. Brown, E.B. Chin, Y. Choi, S.A. Khairallah, J.T. McKeown A data-driven, non-linear, parameterized reduced order model of metal 3D printing, Machine Learning and the Physical Sciences Workshop,
*NeurIPS*,**2023** - T. Wen, K. Lee, Y. Choi Reduced-order modeling for parameterized PDEs via implicit neural representations, Machine Learning and the Physical Sciences Workshop,
*NeurIPS*,**2023** - A. Tran, X. He, D.A. Messenger, Y. Choi, D.M. Bortz Weak-form latent space dynamics identification,
*arXiv preprint*, arXiv:2311.12880**2023** - P.H. Tsai, S.W. Chung, D. Ghosh, J. Loffeld, Y. Choi, J.L. Belof Accelerating kinetic simulations of electrostatic plasmas with reduced-order modeling, Machine Learning and the Physical Sciences Workshop,
*NeurIPS*,**2023** - T. Kadeethum, D. O'Malley, Y. Choi, H.S. Viswanathan, H. Yoon Progressive reduced order modeling: empowering data-driven modeling with selective knowledge transfer,
*arXiv preprint*, arXiv:2310.03770**2023** - C. Vales, Y. Choi, D.M. Copeland, S.W. Cheung Energy conserving quadrature based dimensionality reduction for nonlinear hydrodynamics problems,
*Technical Report*, LLNL-TR-853055**2023** - T. Kadeethum, J.D. Jakeman, Y. Choi, N. Bouklas, H. Yoon, Epistemic uncertainty-aware Barlow twins reduced order modeling for nonlinear contact problems,
*IEEE Access*,**2023** - X. He, Y. Choi, W. Fries, J. Belof, J.S. Chen, gLaSDI: Parametric physics-informed greedy latent space dynamics identification,
*Journal of Computational Physics*, 489, 112267,**2023** - S.W. Cheung, Y. Choi, H.K. Springer, T. Kadeethum, Data-scarce surrogate modeling of shock-induced pore collapse process,
*arXiv preprint*, arXiv:2306.00184,**2023** - A.N. Diaz, Y. Choi, M. Heinkenschloss, A fast and accurate domain-decomposition nonlinear manifold reduced order model,
*arXiv preprint*, arXiv:2305.15163,**2023** - Q.A. Huhn, M.E. Tano, J.C. Ragusa, Y. Choi, Parametric dynamic mode decomposition for reduced order modeling,
*Journal of Computational Physics*, 475, 111852,**2023** - C. G. Petra, M. Salazar De Troya, N. Petra, Y. Choi, G. M. Oxberry, D. Tortorelli, On the implementation of a quasi-Newton interior-point method for PDE-constrained optimization using finite element discretizations,
*Optimization Methods and Software*, 1-32,**2023** - S.W Cheung, Y. Choi, D. Copeland, K. Huynh, Local Lagrangian reduced-order modeling for Rayleigh-Taylor instability by solution manifold decomposition,
*Journal of Computational Physics*, 472, 111655,**2023**

#### 2022

- 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,
*Scientific Reports*, 12(1),**2022** - X. He, Y. Choi, W. Fries, J. Belof, J.S. Chen, Certified data-driven physics-informed greedy auto-encoder simulator,
*arXiv preprint*, arXiv:2211.13698,**2022** - S. McBane, Y. Choi, K. Willcox, Stress-constrained topology optimization of lattice-like structures using component-wise reduced order models,
*Computer Methods in Applied Mechanics and Engineering*, 400, 115525,**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. - 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** - 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** - 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, F. Ballarin, D. O'Malley, Y. Choi, N. Bouklas, H. Yoon, Reduced order modeling with Barlow Twins self-supervised learning: Navigating the space between linear and nonlinear solution manifolds,
*arXiv preprint*, arXiv:2202.05460,**2022** - 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,**2022**. Also available as arXiv:2104.11404. - 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**

#### 2021

- 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** - 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.

#### 2020

- 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**