Publications

2024

  1. J. S. R. Park, S. W. Cheung, Y. Choi, and Y. Shin, tLaSDI: Thermodynamics-informed latent space dynamics identification, Computer Methods in Applied Mechanics and Engineering 429, p117144 2024
  2. 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, SIAM Journal on Scientific Computing, 46, 4, 2024
  3. S.W. Cheung, Y. Choi, H.K. Springer, T. Kadeethum, Data-scarce surrogate modeling of shock-induced pore collapse process, Shock Waves, 34, p237--256, 2024
  4. T. Kadeethum, D. O'Malley, Y. Choi, H.S. Viswanathan, H. Yoon Progressive transfer learning for advancing machine learning-based reduced-order modeling, Scientific Reports, 14, 15731 2024
  5. S. 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 simulators via reduced order modeling and domain decomposition, Computer Methods in Applied Mechanics and Engineering, 427, 117041, 2024
  6. A. Tran, X. He, D. A. Messenger, Y. Choi, D. M. Bortz, Weak-form latent space dynamics identification, Computer Methods in Applied Mechanics and Engineering, 427, 116998, 2024
  7. X. He, A. Tran, D. M. Bortz, Y. Choi, Physics-informed active learning with simultaneous weak-form latent space dynamics identification, arXiv preprint, arXiv:2407.00337, 2024
  8. Y. Kim, Y. Choi, B. Yoo, Gappy AE: A nonlinear approach for Gappy data reconstruction using auto-encoder, Computer Methods in Applied Mechanics and Engineering, 426, 116978, 2024
  9. A. N. Diaz, Y. Choi, M. Heinkenschloss A fast and accurate domain decomposition nonlinear manifold reduced order model, Computer Methods in Applied Mechanics and Engineering, 425, 116943, 2024
  10. C. Bonneville, X. He, A. Tran, J. S. Park, W. Fries, D. A. Messenger, S. W. Cheung, D. M. Bortz, D. Ghosh, J. S. Chen, A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling, arXiv preprint arXiv:2403.10748 2024
  11. 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

  1. Y. Kim, Y. Choi, B. Yoo, Gappy data reconstruction using unsupervised learning for digital twin, arXiv preprint, arXiv:2312.07902 2023
  2. 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
  3. 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
  4. A.N. Diaz, Y. Choi, M. Heinkenschloss Nonlinear-manifold reduced order models with domain decomposition, Machine Learning and the Physical Sciences Workshop, NeurIPS, 2023
  5. 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
  6. 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
  7. 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
  8. A. Tran, X. He, D.A. Messenger, Y. Choi, D.M. Bortz Weak-form latent space dynamics identification, arXiv preprint, arXiv:2311.12880 2023
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. A.N. Diaz, Y. Choi, M. Heinkenschloss, A fast and accurate domain-decomposition nonlinear manifold reduced order model, arXiv preprint, arXiv:2305.15163, 2023
  15. 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
  16. 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
  17. 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

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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.
  6. 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
  7. 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.
  8. 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
  9. 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.
  10. 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

  1. 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
  2. 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
  3. S. McBane, Y. Choi, Component-wise reduced order model lattice-type structure design, Computer Methods in Applied Mechanics and Engineering, 381, 113813, 2021
  4. 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
  5. 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

  1. 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
  2. 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.
  3. 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

  1. 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
  2. Y. Choi, G. Oxberry, D. Whit, T. Kirchdoerfer, Accelerating design optimization using reduced order models, arXiv preprint, arXiv:1909.11320, 2019
  3. K. Carlberg, Y. Choi, S. Sargsyan, Conservative model reduction for finite-volume models, Journal of Computational Physics, 371, p280-314, 2018
  4. 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
  5. D. Amsallem, M. Zahr, Y. Choi, C. Farhat, Design optimization using hyper-reduced-order models, Structural and Multidisciplinary Optimization, 51, p919-940, 2015