Publications

  1. A.N.M. Nafiz Abeer, Sanket Jantre, Nathan M. Urban, and Byung-Jun Yoon, Leveraging active subspaces to capture epistemic model uncertainty in deep generative models for molecular design, 2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP), 1-6, 2024.
  2. D. Agarwal and G. Biros, Numerical simulation of an extensible capsule using regularized Stokes kernels and overset finite differences, Journal of Computational Physics, 509, 40 pages, 2024.
  3. Nick Alger, Tucker Hartland, Noemi Petra, and 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.
  4. Nick Alger, Blake Christierson, Peng Chen, and Omar Ghattas, Tucker Tensor Train Taylor Series, arXiv:2603.21141, 2026.
  5. Alessandro Alla, Rudy Geelen, and Hannah Lu, Efficient data-driven regression for reduced-order modeling of spatial pattern formation, Journal of Computational Physics, 557:114815, 2026.
  6. Nicole Aretz and Karen Willcox, Nested operator inference for adaptive data-driven learning of reduced-order models, Advances in Computational Mathematics, 52:49, 2026.
  7. Nicole Aretz, Thomas Lynn, Karen Willcox, and Sven Leyffer, Optimal Experimental Design of a Moving Sensor for Linear Bayesian Inverse Problems, arXiv:2509.15961, 2025.
  8. Nicole Aretz and Karen Willcox, Multifidelity Proper Orthogonal Decomposition, arXiv:2605.29213, 2026, submitted to SIAM Journal on Scientific Computing.
  9. Nicole Aretz and Karen Willcox, Enforcing structure in data-driven reduced modeling through nested Operator Inference, 2024 IEEE 63rd Conference on Decision and Control (CDC), 8046-8053, 2024. DOI: 10.1109/CDC56724.2024.10885857.
  10. Nicole Aretz, Peng Chen, Denise Degen, and Karen Veroy, A greedy sensor selection algorithm for hyperparameterized linear Bayesian inverse problems with correlated noise models, Journal of Computational Physics, 498:112599, 2024.
  11. Nicole Aretz, Max Gunzburger, Mathieu Morlighem, and Karen Willcox, Multifidelity Uncertainty Quantification for Ice Sheet Simulations, Computational Geosciences, 29(1):5, 2025.
  12. Hikmet Alperen Aydin and George Biros, HERMES: A fast transient heat transfer solver for metal additive manufacturing, Computer Methods in Applied Mechanics and Engineering, 452:118673, 2026.
  13. Ricardo Baptista, Lianghao Cao, Joshua Chen, Omar Ghattas, Fengyi Li, Youssef M. Marzouk, and 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.
  14. Ricardo Baptista, Bamdad Hosseini, Nikola Kovachki, Youssef Marzouk, and Amir Sagiv, An approximation theory framework for measure-transport sampling algorithms, Mathematics of Computation, 2024.
  15. Ricardo Baptista, Bamdad Hosseini, Nikola Kovachki, and 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.
  16. Joshua Barnett, Irina Tezaur, and 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.
  17. Ayoub Belhadji, Daniel Sharp, and Youssef Marzouk, Weighted quantization using MMD: From mean field to mean shift via gradient flows, The 29th International Conference on Artificial Intelligence and Statistics (AISTATS), 2026.
  18. Ayoub Belhadji, Daniel Sharp, and Youssef M. Marzouk, To discretize continually: Mean shift interacting particle systems for Bayesian inference, arXiv:2605.14142, 2026.
  19. P. Bochev, B. Paskaleva, and L. Musson, Gray box photocurrent models: a data-driven approach for compact radiation modeling, 2025 International Compact Modeling Conference (ICMC), 2025.
  20. P. Bochev, B. Paskaleva, L. Musson, and J. Young, A compact Gray Box Model for neutron-gamma environments, Journal of Radiation Effects: Research and Engineering, 44(1), 2026.
  21. Pavel Bochev, Justin Owen, and Paul Kuberry, Dynamic flux surrogate-based partitioned methods for interface problems, Computer Methods in Applied Mechanics and Engineering, 429:117115, 2024.
  22. Michael Brennan, Ricardo Baptista, and Youssef Marzouk, Dimension reduction via score ratio matching, Transactions on Machine Learning Research, 2025.
  23. Andrey Bryutkin, Matthew E. Levine, Iñigo Urteaga, and Youssef Marzouk, Canonical Bayesian Linear System Identification, submitted to SIAM/ASA Journal on Uncertainty Quantification, 2025.
  24. Lianghao Cao, Joshua Chen, Michael Brennan, Thomas O'Leary-Roseberry, Youssef Marzouk, and Omar Ghattas, LazyDINO: Fast, Scalable, and Efficiently Amortized Bayesian Inversion via Structure-Exploiting and Surrogate-Driven Measure Transport, Journal of Machine Learning Research, 27(24):1-71, 2026.
  25. Lianghao Cao, Thomas O'Leary-Roseberry, Prashant K. Jha, J. Tinsley Oden, and Omar Ghattas, Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problems, Journal of Computational Physics, 486:112104, 2023.
  26. Lianghao Cao, Keyi Wu, J. Tinsley Oden, Peng Chen, and 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.
  27. Lianghao Cao, Thomas O'Leary-Roseberry, and Omar Ghattas, Derivative-informed neural operator acceleration of geometric MCMC for infinite-dimensional Bayesian inverse problems, Journal of Machine Learning Research, 26(78):1-68, 2025.
  28. Nisha Chandramoorthy, Florian Schaefer, and Youssef Marzouk, Score Operator Newton transport, International Conference on Artificial Intelligence and Statistics (AISTATS), 2024.
  29. Youguang Chen and George Biros, Extensions of the Regret-Minimization Algorithm for Optimal Design, SIAM Journal on Matrix Analysis and Applications, 47(2):976-1027, 2026.
  30. Youguang Chen, William Ruys, and George Biros, KNN-DBSCAN: a DBSCAN in high dimensions, ACM Transactions on Parallel Computing, 12(1):1-27, 2025.
  31. Youguang Chen and George Biros, Proximal-IMH: Proximal Posterior Proposals for Independent Metropolis-Hastings with Approximate Operators, ICML 2026, Forty-third International Conference on Machine Learning, 2026.
  32. 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.
  33. James Cheung, Mauro Perego, Pavel Bochev, and Max Gunzburger, A coupling approach for linear elasticity problems with spatially non-coincident discretized interfaces, Journal of Computational and Applied Mathematics, 425:115027, 2023.
  34. Davin Choo, Kirankumar Shiragur, and Caroline Uhler, Causal Discovery under Off-Target Interventions, International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 238:1621-1629, 2024.
  35. A. de Castro and P. Kuberry, Minimally Intrusive Data-Driven Approximation of Schur Complement-based Coupling Operators for Heterogeneous Numerical Methods, Numerical Methods for Partial Differential Equations, submitted, 2026.
  36. Amy de Castro, Paul Kuberry, Irina Tezaur, and 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.
  37. Amy de Castro, Pavel Bochev, Paul Kuberry, and Irina Tezaur, Explicit synchronous partitioned scheme for coupled reduced order models based on composite reduced bases, Computer Methods in Applied Mechanics and Engineering, 417B:116398, 2023.
  38. Amy de Castro, Paul Kuberry, Irina Tezaur, and 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.
  39. Amy de Castro and 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, 2024.
  40. A. Diaz, J. Needels, I. Tezaur, and P. Blonigan, Kernel manifolds: nonlinear-augmentation dimensionality reduction using reproducing kernel Hilbert spaces, International Journal for Numerical Methods in Engineering, 126(24):e70230, 2025.
  41. Christopher Eldred, Francois Gay-Balmaz, and Vakhtang Putkaradze, CLPNets: Coupled Lie-Poisson Neural Networks for Multi-Part Hamiltonian Systems with Symmetries, submitted to Neural Networks, 2024.
  42. Mingzhou Fan, Ruida Zhou, Chao Tian, and Xiaoning Qian, Path-Guided Particle-based Sampling, The 41st International Conference on Machine Learning (ICML), 2024.
  43. Mingzhou Fan, Byung-Jun Yoon, Edward R. Dougherty, Nathan M. Urban, Francis J. Alexander, Raymundo Arroyave, and Xiaoning Qian, Multi-fidelity Bayesian Optimization with Multiple Information Sources of Input-dependent Fidelity, The 40th International Conference on Uncertainty in Artificial Intelligence (UAI), PMLR 244:1271-1293, 2024.
  44. Katharine E. Fisher, Matthew T. C. Li, Youssef Marzouk, and Timo Schorlepp, Precise asymptotic analysis of Sobolev training for random feature models, submitted to SIAM Journal on Mathematics of Data Science, 2025.
  45. Katharine Fisher and Youssef Marzouk, Can Bayesian neural networks make confident predictions? NeurIPS 2024 Workshop on Mathematics of Modern Machine Learning (M3L), 2024 (also arXiv:2501.11773, 2025).
  46. Marc Franquesa Monés, Jiaqi Zhang, and Caroline Uhler, On the Number of Conditional Independence Tests in Constraint-based Causal Discovery, The 29th International Conference on Artificial Intelligence and Statistics (AISTATS), 2026.
  47. Rudy Geelen, Laura Balzano, and Karen Willcox, Learning latent representations in high-dimensional state spaces using polynomial manifold constructions, 2023 62nd IEEE Conference on Decision and Control (CDC), 2023.
  48. Rudy Geelen, Laura Balzano, Stephen Wright, and Karen Willcox, Learning physics-based reduced-order models from data using nonlinear manifolds, Chaos: An Interdisciplinary Journal of Nonlinear Science, 34(3), 2024.
  49. Alexander D. Gilbert, Frances Y. Kuo, Dirk Nuyens, Graham Pash, Ian H. Sloan, and Karen E. Willcox, Quasi-Monte Carlo methods for uncertainty quantification of tumor growth modeled by a parametric semi-linear parabolic reaction-diffusion equation, arXiv:2509.25753, 2025, to appear in SIAM/ASA Journal on Uncertainty Quantification.
  50. Leonidas Gkimisis, Nicole Aretz, Marco Tezzele, Thomas Richter, Peter Benner, and Karen E. Willcox, Non-intrusive reduced-order modeling for dynamical systems with spatially localized features, Computer Methods in Applied Mechanics and Engineering, 444:118115, 2025.
  51. Xindi Gong, Dingcheng Luo, Thomas O'Leary-Roseberry, Ruanui Nicholson, and Omar Ghattas, Shape Derivative-Informed Neural Operators with Application to Risk-Averse Shape Optimization, arXiv:2603.03211, 2026.
  52. A. Gruber, R. Roy-Chowdhury, I. Tezaur, and N. Urban, Flexible and Stable Dynamics Discovery with Onsager's Variational Principle, Physica D: Nonlinear Phenomena, submitted, 2026.
  53. Anthony Gruber, Max Gunzburger, Lili Ju, Rihui Lan, and Z. Wang, Multifidelity Monte Carlo estimation for efficient uncertainty quantification in climate-related modeling, Geoscientific Model Development, 16:1213-1229, 2023.
  54. Anthony Gruber and Irina Tezaur, Canonical and non-canonical Hamiltonian operator inference, Computer Methods in Applied Mechanics and Engineering, 416:116334, 2023.
  55. Anthony Gruber and Irina Tezaur, Variationally consistent Hamiltonian model reduction, SIAM Journal on Applied Dynamical Systems, 24(1):376-414, 2025.
  56. J. Hanson, P. Kuberry, B. Paskaleva, and P. Bochev, Non-intrusive data-driven model order reduction for circuits based on Hammerstein architectures, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2024.
  57. Elizabeth Hawkins, Pavel Bochev, and 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, 55-70, 2023.
  58. Elizabeth Hawkins, Paul Kuberry, and Pavel Bochev, An optimization-based coupling of reduced order models with efficient reduced adjoint basis generation approach, submitted to SIAM Journal on Scientific Computing, 2024.
  59. Chujun He, Matthew Amodio, Orr Ashenberg, Kai W. Wucherpfennig, Ramnik J. Xavier, and Caroline Uhler, Multimodal framework for the joint analysis of single-cell RNA and T cell receptor sequencing data predicts T cell response to cancer immunotherapy, Nature Communications, 17:3840, 2026.
  60. Stefan Henneking, Sreeram Venkat, and Omar Ghattas, Goal-oriented real-time Bayesian inference for linear autonomous dynamical systems with application to digital twins for tsunami early warning, Journal of Computational Physics, 552:114682, 2026.
  61. Stefan Henneking, Fabian Kutschera, Sreeram Venkat, Alice-Agnes Gabriel, and Omar Ghattas, Real-time probabilistic tsunami forecasting in Cascadia from sparse offshore pressure observations, arXiv:2603.14966, 2026.
  62. Stefan Henneking, Sreeram Venkat, Veselin Dobrev, John Camier, Tzanio Kolev, Milinda Fernando, Alice-Agnes Gabriel, and Omar Ghattas, Real-Time Bayesian Inference at Extreme Scale: A Digital Twin for Tsunami Early Warning Applied to the Cascadia Subduction Zone, SC'25: Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, 60-71, 2026.
  63. Mathew Hu, Nick Alger, Rami Nammour, and Omar Ghattas, Accelerating seismic inversion and uncertainty quantification with efficient high-rank Hessian approximations, arXiv:2507.10804, 2025 (also circulated as SSRN preprint, DOI: 10.2139/ssrn.6826569).
  64. Xun Huan, Jayanth Jagalur, and Youssef Marzouk, Optimal experimental design: Formulations and computations, Acta Numerica, 33:715-840, 2024.
  65. Luwen Huang, Inderjit Dhillon, and Karen Willcox, Modeling Longitudinal Student Pathways with Explainable Generative Models, in Proceedings of the LAK26: 16th International Learning Analytics and Knowledge Conference, 2026.
  66. Luwen Huang, Michael Kapteyn, and Karen Willcox, Digital Twin: Graph Formulations for Managing Complexity and Uncertainty, 2024 IEEE International Conference on Digital Twin, 2024.
  67. E. Huynh, J. Owen, P. Kuberry, and P. Bochev, A dynamic flux-based surrogate approach to interface couplings involving full-order models and DMD-based surrogates, Computer Science Research Institute Summer Proceedings 2025, Sandia National Laboratories, 2025.
  68. E. Huynh, J. Owen, P. Kuberry, and P. Bochev, Accurate and efficient data-driven partitioned scheme for coupled heterogeneous numerical models, Numerical Methods for Partial Differential Equations, 42(3):e70135, 2026.
  69. Edward Huynh, Pavel Bochev, and Paul Kuberry, A DMD-based partitioned scheme for time-dependent coupled parametric PDEs, Computer Science Research Institute Summer Proceedings 2024, 2024.
  70. Sanket Jantre, Deepak Akhare, Zhiyuan Wang, Xiaoning Qian, and Nathan M. Urban, Data-augmented few-shot neural emulator for computer-model system identification, Machine Learning: Science and Technology, in press, 2026.
  71. Sanket Jantre, Nathan M. Urban, Xiaoning Qian, and Byung-Jun Yoon, Learning active subspaces for effective and scalable uncertainty quantification in deep neural networks, ICASSP 2024 - IEEE International Conference on Acoustics, Speech and Signal Processing, 5330-5334, 2024.
  72. Joseph Kirchhoff, Dingcheng Luo, Thomas O'Leary-Roseberry, and Omar Ghattas, Inference of heterogeneous material properties via infinite-dimensional integrated DIC, arXiv:2408.10217, 2024.
  73. Abhinav Kumar, Kirankumar Shiragur, and Caroline Uhler, Learning Mixtures of Unknown Causal Interventions, Advances in Neural Information Processing Systems (NeurIPS), 37, 2024.
  74. Matthew Li, Youssef Marzouk, and Olivier Zahm, Principal feature detection via ϕ-Sobolev inequalities, Bernoulli, 30(4):2979-3003, 2024.
  75. Fengyi Li and Youssef Marzouk, Diffusion map particle systems for generative modeling, Foundations of Data Science, 7(3):814-837, 2024.
  76. Matthew Li, Tiangang Cui, Fengyi Li, Youssef Marzouk, and Olivier Zahm, Sharp detection of low-dimensional structure in probability measures via dimensional logarithmic Sobolev inequalities, Information and Inference: A Journal of the IMA, 14(3):iaaf021, 2025.
  77. 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.
  78. Sarah Liaw, Rebecca Morrison, Youssef Marzouk, and Ricardo Baptista, Learning Local Neighborhoods of Non-Gaussian Graphical Models, Proceedings of the AAAI Conference on Artificial Intelligence, 39(18):18711-18718, 2025.
  79. Emily Liu, Jiaqi Zhang, and Caroline Uhler, Learning genetic perturbation effects with variational causal inference, PLOS Computational Biology, 22:e1013194, 2026.
  80. Guillermo Lorenzo, David A. Hormuth II, Chengyue Wu, Graham Pash, Anirban Chaudhuri, Ernesto A. B. F. Lima, Lois C. Okereke, Reshmi Patel, Karen Willcox, and Thomas E. Yankeelov, Validating the predictions of mathematical models describing tumor growth and treatment response, in Model Validation and Uncertainty Quantification in Biomechanics: From Soft Biological Tissue to Blood Flow, Elsevier, Chapter 12, 2026 (preprint: arXiv:2502.19333).
  81. Hannah Lu, Lluís Saló-Salgado, and Ruben Juanes, Lithological controls on the permeability of geologic faults: surrogate modeling and sensitivity analysis, Stochastic Environmental Research and Risk Assessment, 40:186, 2026.
  82. Dingcheng Luo, Thomas O'Leary-Roseberry, Peng Chen, and Omar Ghattas, Dimension reduction for derivative-informed operator learning: An analysis of approximation errors, arXiv:2504.08730, 2026, to appear in Journal of Machine Learning Research.
  83. Dingcheng Luo, Lianghao Cao, Peng Chen, Omar Ghattas, and 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.
  84. 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.
  85. Dingcheng Luo, Joshua Chen, Peng Chen, and Omar Ghattas, Gaussian mixture Taylor approximations of risk measures constrained by PDEs with Gaussian random field inputs, arXiv:2408.06615, 2024.
  86. Dingcheng Luo, Thomas O'Leary-Roseberry, Peng Chen, and Omar Ghattas, Efficient PDE-constrained optimization under high-dimensional uncertainty using derivative-informed neural operators, SIAM Journal on Scientific Computing, 47(4):899-931, 2025.
  87. Nguyen Ly, Caroline Tatsuoka, Jai Nagaraj, Jacob Levy, Fernando Palafox, David Fridovich-Keil, and Hannah Lu, Data-Driven Modeling and Correction of Vehicle Dynamics, Journal of Machine Learning for Modeling and Computing, 7(2), 2026.
  88. Youssef Marzouk, Zhi (Robert) Ren, Sven Wang, and Jakob Zech, Distribution learning via neural differential equations: a nonparametric statistical perspective, Journal of Machine Learning Research, 25(232):1-61, 2024.
  89. Bijan Mazaheri, Chandler Squires, and Caroline Uhler, Synthetic Potential Outcomes and Causal Mixture Identifiability, International Conference on Artificial Intelligence and Statistics (AISTATS), 2025.
  90. T. Meissner, P. Bochev, and B. Paskaleva, Sparse state-space models for circuits under ionizing radiation, Journal of Radiation Effects: Research and Engineering, 44(1), 2026.
  91. T. Meissner, B. Paskaleva, and P. Bochev, A sparse state-space compact modeling approach for electric circuits, Large-Scale Scientific Computations, 3-10, 2026.
  92. T. Meissner, E. Huynh, P. Kuberry, and P. Bochev, A deep least-squares method for the Stokes equations, Computers and Mathematics with Applications, 196:1-12, 2025.
  93. T. Meissner, J. Hanson, J. Young, and P. Bochev, Compact circuit models for EM effects based on statistical decomposition of system dynamics, International Compact Modeling Conference (ICMC), submitted, 2026.
  94. Ian Moore, Christopher Wentland, Anthony Gruber, and Irina Tezaur, Domain decomposition-based coupling of operator inference reduced order models via the Schwarz alternating method, Computer Science Research Institute Summer Proceedings 2024, 2024.
  95. Thomas O'Leary-Roseberry, Peng Chen, Umberto Villa, and Omar Ghattas, Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning, Journal of Computational Physics, 496:112555, 2024.
  96. E. Parish, A. Gruber, P. Blonigan, and I. Tezaur, NN-OpInf: an operator inference approach using structure-preserving composable neural networks, Journal of Computational Physics, submitted, 2026.
  97. Eric Parish, Masayuki Yano, Irina Tezaur, and Traian Iliescu, Residual-based stabilized reduced-order models of transient partial differential equations obtained through discrete and continuous projection, Archives of Computational Methods in Engineering, 32:1885-1929, 2025.
  98. Graham Pash, Umberto Villa, David A. Hormuth II, Thomas E. Yankeelov, and Karen Willcox, Predictive Digital Twins with Quantified Uncertainty for Patient-Specific Decision Making in Oncology, Journal of Computational Physics, 560:114937, 2026.
  99. Rishi Pawar and Pavel Bochev, Operator inference based flux surrogate algorithm for coupled transmission problems, Computer Science Research Institute Summer Proceedings 2024, 2024.
  100. Julie V. Pham, Omar Ghattas, Noel T. Clemens, and Karen E. Willcox, Real-time aerodynamic load estimation for hypersonics via strain-based inverse maps, AIAA Journal, 63(1):91-101, 2025.
  101. Julie Pham, Omar Ghattas, and Karen Willcox, Neural operator-enabled real-time inverse solutions with optimal sensor selection, AIAA SciTech Forum, 2027, to appear.
  102. J. Pham, P. Blonigan, T. O'Leary-Roseberry, O. Ghattas, and K. Willcox, Neural operator-enabled aerodynamic load estimation for hypersonics, AIAA SciTech 2026 Forum, January 2026.
  103. Simone Puel, Thorsten W. Becker, Umberto Villa, Omar Ghattas, and Dunyu Liu, Volcanic arc rigidity variations illuminated by coseismic deformation of the 2011 Tohoku-oki M9, Science Advances, 10(23):4264, 2024.
  104. Yigong Qin, Balasubramanian Radhakrishnan, Stephen DeWitt, and George Biros, GrainGNN: A dynamic graph neural network for predicting 3D grain microstructure, Journal of Computational Physics, 510, 35 pages, 2024.
  105. Amir Hossein Rahmati, Sanket Jantre, Weifeng Zhang, Yucheng Wang, Byung-Jun Yoon, Nathan M. Urban, and Xiaoning Qian, C-LoRA: Contextual low-rank adaptation for uncertainty estimation in large language models, Advances in Neural Information Processing Systems, 38, 2025.
  106. Amir Hossein Rahmati, Nathan M. Urban, Byung-Jun Yoon, and Xiaoning Qian, Cost-effective Reduced-order Modeling via Bayesian Active Learning, NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty (BDU), 2024 (updated preprint: arXiv:2506.22645, 2025).
  107. Amir Hossein Rahmati, Mingzhou Fan, Ruida Zhou, Byung-Jun Yoon, Nathan M. Urban, and Xiaoning Qian, When Uncertainty-based Active Learning May Fail? Pattern Recognition, ICPR 2024 Proceedings, Part I, Lecture Notes in Computer Science 15301, Chapter 6, Springer, 2025.
  108. Amir Hossein Rahmati, Mingzhou Fan, Ruida Zhou, Nathan M. Urban, Byung-Jun Yoon, and Xiaoning Qian, Understanding Uncertainty-based Active Learning Under Model Mismatch, arXiv:2408.13690, 2024 (submitted to IEEE Transactions on Signal Processing).
  109. Álvaro Ribot, Chandler Squires, and Caroline Uhler, Predicting Context-Dependent Drug Effects using Causal Matrix Completion, NeurIPS, under review, 2023.
  110. Álvaro Ribot, Chandler Squires, and Caroline Uhler, Causal imputation for counterfactual SCMs: Bridging graphs and latent factor models, Proceedings of Machine Learning Research (CLeaR 2024), 2024.
  111. C. Rodriguez, I. Tezaur, A. Mota, A. Gruber, E. Parish, and C. Wentland, Transmission Conditions for the Non-Overlapping Schwarz Coupling of Full Order and Operator Inference Models, Computer Science Research Institute Summer Proceedings 2025, Sandia National Laboratories, 2025.
  112. R. Roy-Chowdhury, I. Tezaur, N. Urban, and A. Gruber, Onsager's variational principle for solving inverse problems, Computer Science Research Institute Summer Proceedings 2025, Sandia National Laboratories, 2025.
  113. Bassel Saleh, Aaron Zimmerman, Peng Chen, and Omar Ghattas, Tempered multifidelity importance sampling for gravitational wave parameter estimation, Physical Review D, 110:104037, 2024.
  114. G. Sambataro and I. Tezaur, On the role of relaxation and acceleration in the non-overlapping Schwarz alternating method for coupling, Journal of Computational and Applied Mathematics, submitted, 2026.
  115. Facundo Sapienza, Jordi Bolibar, Frank Schäfer, Brian Groenke, Avik Pal, Victor Boussange, Patrick Heimbach, Giles Hooker, Fernando Pérez, Per-Olof Persson, and Christopher Rackauckas, Differentiable Programming for Differential Equations: A Review, arXiv:2406.09699, 2024 (revised).
  116. Vignesh Sella, Julie Pham, Karen E. Willcox, and Anirban Chaudhuri, Projection-based multifidelity linear regression for data-scarce applications, Machine Learning for Computational Science and Engineering, 1(47), 2026.
  117. Bowen Shi, Sreeram Venkat, Stefan Henneking, and Omar Ghattas, Rapid Earthquake-to-Tsunami Waveform Generation via Large-Scale Multi-GPU FFT Convolution Applied to the Cascadia Subduction Zone, submitted, 2026.
  118. Kirankumar Shiragur, Jiaqi Zhang, and Caroline Uhler, Meek separators and their applications in targeted causal discovery, Advances in Neural Information Processing Systems (NeurIPS) 37, 2023.
  119. Divya Shyamal, Jiaqi Zhang, and Caroline Uhler, Probabilistic Factorial Experimental Design for Combinatorial Interventions, International Conference on Machine Learning (ICML), PMLR 267:55474-55492, 2025.
  120. William Snyder, Irina Tezaur, and 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, 390-411, 2023.
  121. Chad Sockwell, Pavel Bochev, Kara Peterson, and Paul Kuberry, Interface flux recovery framework for constructing partitioned heterogeneous time-integration methods, Numerical Methods for Partial Differential Equations, 39(5):3572-3593, 2023.
  122. Nils Sturma, Chandler Squires, Mathias Drton, and Caroline Uhler, Unpaired multi-domain causal representation learning, Advances in Neural Information Processing Systems (NeurIPS) 37, 2023.
  123. I. Tezaur, E. Parish, A. Gruber, I. Moore, C. Wentland, and A. Mota, Hybrid coupling with Operator Inference and the overlapping Schwarz alternating method, International Journal for Numerical Methods in Engineering, to appear, 2026.
  124. Sana Tonekaboni, Sam Freesun Friedman, Xinyi Zhang, Mahnaz Maddah, and Caroline Uhler, A Representation Fusion Framework for Decoupling Diagnostic Information in Multimodal Learning, npj Digital Medicine, 8:765, 2025.
  125. Panos Tsimpos, Zhi Ren, Jakob Zech, and Youssef Marzouk, Optimal Scheduling of Dynamic Transport, Conference on Learning Theory (COLT), PMLR 291:5441-5505, 2025.
  126. Jiqun Tu, Ian Karlin, John Camier, Veselin Dobrev, Tzanio Kolev, Stefan Henneking, and Omar Ghattas, Accelerating high-order finite element simulations at extreme scale with FP64 tensor cores, in ISC High Performance 2026 Research Paper Proceedings (40th International Conference), 1-12, 2026.
  127. Caroline Uhler, Causal Structure and Representation Learning with Biomedical Applications, Proceedings of the International Congress of Mathematicians, 2026.
  128. Sreeram Venkat, Stefan Henneking, and Omar Ghattas, Sensor Placement for Tsunami Early Warning via Large-Scale Bayesian Optimal Experimental Design, arXiv:2604.08812, 2026, to appear in Proceedings of SC26.
  129. Sreeram Venkat, Milinda Fernando, Stefan Henneking, and 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, 47(5):B1201-B1226, 2025.
  130. A. Vijaywargiya, S. McQuarrie, and A. Gruber, Tensor parametric Hamiltonian operator inference, SIAM Journal on Applied Dynamical Systems, 2026.
  131. Arjun Vijaywargiya, Shane A. McQuarrie, and Anthony Gruber, Tensor Parametric Operator Inference with Structure Information, Computer Science Research Institute Summer Proceedings 2024, 2024.
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