1. C. Bonneville, Y. Choi, D. Ghosh, J.L. Belof, GPLaSDI: Gaussian Process-based Interpretable Latent Space Dynamics Identification through Deep Autoencoder, arXiv preprint, arXiv:2308.05882, 2023
  2. 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
  3. 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
  4. 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
  5. A.N. Diaz, Y. Choi, M. Heinkenschloss, A fast and accurate domain-decomposition nonlinear manifold reduced order model, arXiv preprint, arXiv:2305.15163, 2023
  6. 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
  7. 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
  8. 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


  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. 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
  8. 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.
  9. 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
  10. 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.
  11. 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


  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.


  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