Publications

Refereed Journal and Conference Articles

  1. D. Agarwal and G. Biros, Numerical simulation of an extensible capsule using regularized Stokes kernels and overset finite differences, Journal of Computational Physics, volume 509, 40 pages, 2024
  2. Nick Alger, Tucker Hartland, Noemi Petra, Omar Ghattas, Point spread function approximation of high rank Hessians with locally supported non-negative integral kernels, SIAM Journal on Scientific Computing, 46(3):A1658-A1689, 2024.
  3. Nicole Aretz and Karen Willcox, Enforcing structure in data-driven reduced modeling through nested Operator Inference, Proceedings of Conference on Decision and Control (CDC24), to appear, December 2024.
  4. Nicole Aretz, Peng Chen, Denise Degen, Karen Veroy, A greedy sensor selection algorithm for hyperparameterized linear Bayesian inverse problems with correlated noise models, Journal of Computational Physics, 498:112599, 2024.
  5. Ricardo Baptista, Lianghao Cao, Joshua Chen, Omar Ghattas, Fengyi Li, Youssef M. Marzouk, J. Tinsley Oden, Bayesian model calibration for block copolymer self-assembly: Likelihood-free inference and expected information gain computation via measure transport, Journal of Computational Physics, 503:112844, 2024.
  6. Ricardo Baptista, Bamdad Hosseini, Nikola Kovachki, Youssef Marzouk, Amir Sagiv. An approximation theory framework for measure-transport sampling algorithms, Mathematics of Computation, 2024.
  7. Ricardo Baptista, Bamdad Hosseini, Nikola Kovachki, Youssef Marzouk. Conditional sampling with monotone GANs: from generative models to likelihood-free inference. SIAM/ASA Journal on Uncertainty Quantification, 12(3): 868-900, 2024.
  8. Pavel Bochev, Justin Owen, Paul Kuberry, Dynamic flux surrogate-based partitioned methods for interface problems, Computer Methods in Applied Mechanics and Engineering, 429:117115, 2024.
  9. Youguang Chen and George Biros, FIRAL: An Active Learning Algorithm for Multinomial Logistic Regression, NeurIPS'23, Thirty-seventh Conference on Neural Information Processing Systems, December 2023.
  10. Tianyu Liang, Chao Chen, Per Gunnar Martinsson and George Biros, A distributed-memory parallel algorithm for discretized integral equations using Julia, 38th IEEE International Parallel & Distributed Processing Symposium, IPDPS'24, May, 2024.
  11. Lianghao Cao, Thomas O’Leary-Roseberry, Prashant K. Jha, J. Tinsley Oden, Omar Ghattas, Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problems, Journal of Computational Physics, 486:112104, 2023.
  12. Lianghao Cao, Keyi Wu, J. Tinsley Oden, Peng Chen, Omar Ghattas, Bayesian model calibration for diblock copolymer thin film self-assembly using power spectrum of microscopy data and machine learning surrogate, Computer Methods in Applied Mechanics and Engineering, 116349, 2023.
  13. Nisha Chandramoorthy, Florian Schaefer, Youssef Marzouk. Score Operator Newton transport, International Conference on Artificial Intelligence and Statistics (AISTATS), 2024.
  14. James Cheung, Mauro Perego, Pavel Bochev, Max Gunzburger, A coupling approach for linear elasticity problems with spatially non-coincident discretized interfaces, Journal of Computational and Applied Mathematics, 425:115027, 2023.
  15. Amy de Castro, Paul Kuberry, Irina Tezaur, Pavel Bochev, A Novel Partitioned Approach for Reduced Order Model - Finite Element Model (ROM-FEM) and ROM-ROM Coupling, Proceedings of the ASCE Earth and Space 18th Biennial International Conference, 475-489, 2023.
  16. Amy de Castro, Pavel Bochev, Paul Kuberry, Irina Tezaur, Explicit synchronous partitioned scheme for coupled reduced order models based on composite reduced bases, Computer Method in Applied Mechanics and Engineering, 417B:116398, 2023.
  17. Mingzhou Fan, Byung-Jun Yoon, Edward R. Dougherty, Nathan M. Urban, Francis J. Alexander, Raymundo Arroyave, Xiaoning Qian, Multi-fidelity Bayesian Optimization with Multiple Information Sources of Input-dependent Fidelity, The 40th International Conference on Uncertainty in Artificial Intelligence (UAI), 2024.
  18. Katharine Fisher, Youssef Marzouk. Can Bayesian neural networks make confident predictions?, NeurIPS 2024 Workshop on Mathematics of Modern Machine Learning (M3L), to appear, 2024.
  19. Rudy Geelen, Laura Balzano, Karen Willcox, Learning latent representations in high-dimensional state spaces using polynomial manifold constructions, 2023 62nd IEEE Conference on Decision and Control (CDC), 2023.
  20. Rudy Geelen, Laura Balzano, Stephen Wright, Karen Willcox, Learning physics-based reduced-order models from data using nonlinear manifolds, Chaos: An Interdisciplinary Journal of Nonlinear Science, 34(3), 2024.
  21. Anthony Gruber, Max Gunzburger, Lili Ju, Rihui Lan, Z. Wang, Multifidelity Monte Carlo estimation for efficient uncertainty quantification in climate-related modeling, Geoscientific Model Development, 16:1213-1229, 2023.
  22. Anthony Gruber, Irina Tezaur, Canonical and non-canonical Hamiltonian operator inference, Computer Methods in Applied Mechanics and Engineering, 416:116334, 2023.
  23. Anthony Gruber, Irina Tezaur, Variationally consistent Hamiltonian model reduction, SIAM Journal on Applied Dynamical Systems, in press, 2024.
  24. Xun Huan, Jayanth Jagalur, Youssef Marzouk. Optimal experimental design: Formulations and computations, Acta Numerica, 33:715-840, 2024.
  25. Luwen Huang, Michael Kapteyn, Karen Willcox, Digital Twin: Graph Formulations for Managing Complexity and Uncertainty, Proceedings of IEEE International Conference on Digital Twin, to appear, December 2024.
  26. Matthew Li, Youssef Marzouk, Olivier Zahm. Principal feature detection via ϕ-Sobolev inequalities, Bernoulli, 30(4): 2979-3003, 2024.
  27. Dingcheng Luo, Lianghao Cao, Peng Chen, Omar Ghattas, J. Tinsley Oden, Optimal design of chemoepitaxial guideposts for the directed self-assembly of block copolymer systems using an inexact-Newton algorithm, Journal of Computational Physics, 485:112101, 2023.
  28. Dingcheng Luo, Peng Chen, Thomas O’Leary-Roseberry, Umberto Villa, and Omar Ghattas, SOUPy: Stochastic PDE-constrained optimization under high-dimensional uncertainty in Python, Journal of Open Source Software, 9(99):6101, 2024.
  29. Youssef Marzouk, Zhi (Robert) Ren, Sven Wang, Jakob Zech, Distribution learning via neural differential equations: a nonparametric statistical perspective, Journal of Machine Learning Research, 25(232): 1-61, 2024.
  30. Eric Parish, Masayuki Yano, Irina Tezaur, Traian Iliescu, Residual-based stabilized reduced-order models of transient partial differential equations obtained through discrete and continuous projection, Archives on Computational Methods in Engineering, in press, 2024.
  31. Simone Puel, Thorsten W. Becker, Umberto Villa, Omar Ghattas, Dunyu Liu, Volcanic arc rigidity variations illuminated by coseismic deformation of the 2011 Tohoku-oki M9, Science Advances, 10(23):4264, 2024.
  32. Yigong Qin, Balasubramanian Radhakrishnan, Stephen DeWitt, and George Biros, GrainGNN: A dynamic graph neural network for predicting 3D grain microstructure, Journal of Computational Physics, volume 510, 35 pages, 2024.
  33. Amir Hossein Rahmati, Nathan M. Urban, Byung-Jun Yoon, Xiaoning Qian, Cost-effective Reduced-order Modeling via Bayesian Active Learning, NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty (BDU), 2024.
  34. Amir Hossein Rahmati, Mingzhou Fan, Ruida Zhou, Byung-Jun Yoon, Nathan M. Urban, Xiaoning Qian, When Uncertainty-based Active Learning May Fail? The 27th International Conference on Pattern Recognition (ICPR), 2024.
  35. Álvaro Ribot, Chandler Squires, Caroline Uhler, Causal imputation for counterfactual SCMs: Bridging graphs and latent factor models, Proceedings of Machine Learning Research (CLeaR 2024), 2024.
  36. Kirankumar Shiragur, Jiaqi Zhang, Caroline Uhler, Meek separators and their applications in targeted causal discovery, Advances in Neural Information Processing Systems (NeurIPS) 37, 2023.
  37. Chad Sockwell, Pavel Bochev, Kara Peterson, Paul Kuberry, Interface flux recovery framework for constructing partitioned heterogeneous time-integration methods, Numerical Methods for Partial Differential Equations, 39(5):3572-3593, 2023.
  38. Nils Sturma, Chandler Squires, Mathias Drton, Caroline Uhler, Unpaired multi-domain causal representation learning, Advances in Neural Information Processing Systems (NeurIPS) 37, 2023.
  39. Jiaqi Zhang, Kristjan Greenewald, Chandler Squires, Akash Srivastava, Karthikeyan Shanmugam, Caroline Uhler, Identifiability guarantees for causal disentanglement from soft interventions, Advances in Neural Information Processing Systems (NeurIPS) 37, 2023.
  40. Jiaqi Zhang, Kirankumar Shiragur, Caroline Uhler, Membership testing in Markov equivalence classes via independence queries, International Conference on Artificial Intelligence and Statistics (AISTATS), 2024.

Preprints, Submitted, and Other Conferences

  1. Nicole Aretz, Max Gunzburger, Mathieu Morlinghem, Karen Willcox, Multifidelity Uncertainty Quantification for Ice Sheet Simulations, Computational Geosciences, submitted, 2024.
  2. Joshua Barnett, Irina Tezaur, Alejandro Mota, The Schwarz alternating method for the seamless coupling of nonlinear reduced order models and full order models, Computer Science Research Institute Summer Proceedings 2022, S. Seritan and J.D. Smith, eds., Technical Report SAND 2022-10280R, Sandia National Laboratories, 2022.
  3. Michael Brennan, Ricardo Baptista, Youssef Marzouk, Dimension reduction via score-ratio matching, Transactions on Machine Learning Research, submitted, 2024.
  4. Lianghao Cao, Thomas O'Leary-Roseberry, Omar Ghattas, Derivative-informed neural operator acceleration of geometric MCMC for infinite-dimensional Bayesian inverse problems, Journal of Machine Learning Research, submitted, 2024.
  5. Amy de Castro, Paul Kuberry, Irina Tezaur, Pavel Bochev, A synchronous partitioned scheme for coupled reduced order models based on separate reduced order bases for interface and interior variables, Computer Science Research Institute Summer Proceedings 2022, S. Seritan and J.D. Smith, eds., Technical Report SAND 2022-10280R, Sandia National Laboratories, 2022.
  6. Amy de Castro, Paul Kuberry, Comparing Stability of Partitioned Heterogeneous Time-Integration Methods Involving Index-2 DAEs Resulting from High-Order Adams-Moulton and Backward Difference Formula Time Integration Schemes, Computer Science Research Institute Summer Proceedings, to appear, 2024.
  7. Christopher Eldred, Francois Gay-Balmaz, Vakhtang Putkaradze, CLPNets: Coupled Lie-Poisson Neural Networks for Multi-Part Hamiltonian Systems with Symmetries, Neural Networks, submitted, 2024.
  8. Elizabeth Hawkins, Pavel Bochev, Paul Kuberry, An optimization-based approach for coupling projection-based reduced order models, Computer Science Research Institute Summer Proceedings 2023, S. Seritan and B. Reuter, eds., Technical Report SAND 2023-13916R, Sandia National Laboratories, pp. 55--70, 2023.
  9. Elizabeth Hawkins, Paul Kuberry, Pavel Bochev, An optimization-based coupling of reduced order models with efficient reduced adjoint basis generation approach, SIAM Journal on Scientific Computing, submitted, 2024.
  10. Edward Huynh, Pavel Bochev, Paul Kuberry, A DMD-based partitioned scheme for time-dependent coupled parametric PDEs, Computer Science Research Institute Summer Proceedings 2024, to appear, 2024.
  11. Joseph Kirchhoff, Dingcheng Luo, Thomas O'Leary-Roseberry, and Omar Ghattas, Inference of heterogeneous material properties via infinite-dimensional integrated DIC, 2024.
  12. Fengyi Li and Youssef Marzouk, Diffusion map particle systems for generative modeling, Foundations of Data Science, submitted, 2024.
  13. Matthew Li, Tiangang Cui, Fengyi Li, Youssef Marzouk, Olivier Zahm, Sharp detection of low-dimensional structure in probability measures via dimensional logarithmic Sobolev inequalities, Information and Inference: A Journal of the IMA, submitted, 2024.
  14. Dingcheng Luo, Joshua Chen, Peng Chen, Omar Ghattas, Gaussian mixture Taylor approximations of risk measures constrained by PDEs with Gaussian random field inputs, 2024.
  15. Dingcheng Luo, Thomas O'Leary-Roseberry, Peng Chen, Omar Ghattas, Efficient PDE-constrained optimization under high-dimensional uncertainty using derivative-informed neural operators, SIAM Journal on Scientific Computing, submitted, 2023.
  16. Ian Moore, Christopher Wentland, Anthony Gruber, Irina Tezaur, Domain decomposition-based coupling of operator inference reduced order models via the Schwarz alternating method, Computer Science Research Institute Summer Proceedings 2024, to appear, 2024.
  17. Rishi Pawar, Pavel Bochev, Operator inference based flux surrogate algorithm for coupled transmission problems, Computer Science Research Institute Summer Proceedings 2024, to appear, 2024.
  18. Amir Hossein Rahmati, Mingzhou Fan, Ruida Zhou, Nathan M. Urban, Byung-Jun Yoon, Xiaoning Qian, Understanding Uncertainty-based Active Learning Under Model Mismatch, IEEE Transactions on Signal Processing, submitted, 2024.
  19. William Snyder, Irina Tezaur, Christopher Wentland, Domain decomposition-based coupling of physics-informed neural networks via the Schwarz alternating method, Computer Science Research Institute Summer Proceedings 2023, S. Seritan and B. Reuter, eds., Technical Report SAND 2023-13916R, Sandia National Laboratories, pp. 390--411, 2023.
  20. Sreeram Venkat, Milinda Fernando, Stefan Henneking, Omar Ghattas, Fast and scalable FFT-based GPU-accelerated algorithms for Hessian actions arising in linear inverse problems governed by autonomous dynamical systems, SIAM Journal on Scientific Computing, submitted, 2024.
  21. Arjun Vijaywargiya, Shane A. McQuarrie, Anthony Gruber, Tensor Parametric Operator Inference with Structure Information, Computer Science Research Institute Summer Proceedings 2024, to appear, 2024.
  22. Christopher Wentland, Francesco Rizzi, Joshua Barnett, Irina Tezaur, The role of interface boundary conditions and sampling strategies for Schwarz-based coupling of projection-based reduced order models, Journal of Computational and Applied Mathematics, submitted, 2024.