Research focus
The CMATD chair’s research interests are broadly in computational mathematics for machine learning and scientific computing. All research topics are multidisciplinary in the sense that they intersect with various aspects of mathematics of data science, computational statistics, numerical analysis, and computer science.
Selected preprints and submitted articles
| [1] | One-Step Generative Surrogate Models via Block-Triangular Joint Drifting. arXiv, 2609.26435, 2026. |
| [2] | Randomized time stepping of nonlinearly parametrized solutions of evolution problems. arXiv, 2512.19009, 2025. |
| [3] | DICE: Discrete inverse continuity equation for learning population dynamics. arXiv, 2507.05107, 2025. |
Publications
| [1] | First-Order Trajectory Matching: Fast Ensemble Predictions of Chaotic, Turbulent, Stochastic Systems. NeurIPS, 2026. |
| [2] | Operator Inference Aware Quadratic Manifolds with Isotropic Reduced Coordinates for Nonintrusive Model Reduction. Advances in Computational Mathematics, , 2026. (accepted). |
| [3] | Leveraging Gauge Freedom for Learning Non-Gradient Population Dynamics of Stochastic Systems. International Conference on Machine Learning (ICML), 2026. |
| [4] | Two-Parameter Flows for Learning Population Dynamics of Physical Systems. International Conference on Machine Learning (ICML), 2026. |
| [5] | Stochastic Lifting for Generating Trajectories of Stochastic Physical Systems. International Conference on Machine Learning (ICML), 2026. |
| [6] | A Dirac-Frenkel-Onsager principle: Instantaneous residual minimization with gauge momentum for nonlinear parametrizations of PDE solutions. International Conference on Machine Learning (ICML), 2026. (Spotlight). |
| [7] | Greedy construction of quadratic manifolds for nonlinear dimensionality reduction and nonlinear model reduction. SIAM Journal on Mathematics of Data Science, 2026. |
| [8] | Nonlinear model reduction for transport-dominated problems. Acta Numerica, 35, 2026. |
| [9] | Filtered Neural Galerkin model reduction schemes for efficient propagation of initial condition uncertainties in digital twins. Journal of Computational Physics, 559, 2026. |
| [10] | An adaptive data sampling strategy for stabilizing dynamical systems via controller inference. SIAM Journal on Scientific Computing, 2026. |
| [11] | Hankel Singular Value Regularization for Highly Compressible State Space Models. NeurIPS, 2025. |
| [12] | Sequential-in-time training of nonlinear parametrizations for solving time-dependent partial differential equations. SIAM Review, 2025. (accepted). |
| [13] | Nonlinear model reduction with Neural Galerkin schemes on quadratic manifolds. Journal of Computational Physics, 2025. (accepted). |
| [14] | Empirical sparse regression on quadratic manifolds. SIAM Journal on Scientific Computing, 2025. (accepted). |
| [15] | Online learning of quadratic manifolds from streaming data for nonlinear dimensionality reduction and nonlinear model reduction. Proceedings of the Royal Society A, 2025. |
| [16] | Uncertainty quantification in coupled wildfire-atmosphere simulations at scale. PNAS Nexus, 2024. |
| [17] | Parametric model reduction of mean-field and stochastic systems via higher-order action matching. NeurIPS, 2024. |
| [18] | System stabilization with policy optimization on unstable latent manifolds. Computer Methods in Applied Mechanics and Engineering, 433, 2024. |
| [19] | Multifidelity Covariance Estimation via Regression on the Manifold of Symmetric Positive Definite Matrices. SIAM Journal on Mathematics of Data Science, , 2024. (accepted). |
| [20] | Nonlinear embeddings for conserving Hamiltonians and other quantities with Neural Galerkin schemes. SIAM Journal on Scientific Computing, 2024. (accepted). |
| [21] | CoLoRA: Continuous low-rank adaptation for reduced implicit neural modeling of parameterized partial differential equations. International Conference on Machine Learning (ICML), 2024. |
| [22] | Further analysis of multilevel Stein variational gradient descent with an application to the Bayesian inference of glacier ice models. Advances in Computational Mathematics, 2024. |
| [23] | Coupling parameter and particle dynamics for adaptive sampling in Neural Galerkin schemes. Physica D, 2024. |
| [24] | Rank-Minimizing and Structured Model Inference. SIAM Journal on Scientific Computing, 2024. |
| [25] | Neural Galerkin Scheme with Active Learning for High-Dimensional Evolution Equations. Journal of Computational Physics, 2023. |
| [26] | Learning Nonlinear Reduced Models from Data with Operator Inference. Annual Review of Fluid Mechanics, 56, 2024. |
| [27] | Randomized Sparse Neural Galerkin Schemes for Solving Evolution Equations with Deep Networks. NeurIPS 2023 (spotlight). |
| [28] | Lookahead data-gathering strategies for online adaptive model reduction of transport-dominated problems. Chaos: An Interdisciplinary Journal of Nonlinear Science, 2023. (accepted). |
| [29] | Meta variance reduction for Monte Carlo estimation of energetic particle confinement during stellarator optimization. Journal of Computational Physics, 2023. |
| [30] | Multi-fidelity covariance estimation in the log-Euclidean geometry. International Conference on Machine Learning (ICML), 2023. |
| [31] | Operator inference with roll outs for learning reduced models from scarce and low-quality data. Computers & Mathematics with Applications, 145, 2023. |
| [32] | Reduced models with nonlinear approximations of latent dynamics for model premixed flame problems. Proceedings of Workshop on Reduced order models; Approximation theory; Machine learning; Surrogates, Emulators and Simulators (RAMSES), 2023. |
| [33] | Active operator inference for learning low-dimensional dynamical-system models from noisy data. SIAM Journal on Scientific Computing, 2023. (accepted). |
| [34] | Context-aware controller inference for stabilizing dynamical systems from scarce data. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2023. (accepted). |
| [35] | Context-aware learning of hierarchies of low-fidelity models for multi-fidelity uncertainty quantification. Computer Methods in Applied Mechanics and Engineering, 2023. (accepted). |
| [36] | Manifold approximations via transported subspaces: Model reduction for transport-dominated problems. SIAM Journal on Scientific Computing, 45, 2023. |
| [37] | On the sample complexity of stabilizing linear dynamical systems from data. Foundations of Computational Mathematics, 2022. (accepted). |
| [38] | Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inference. Computer Methods in Applied Mechanics and Engineering, 2022. (accepted). |
| [39] | Multi-fidelity robust controller design with gradient sampling. SIAM Journal on Scientific Computing, 2022. (accepted). |
| [40] | Context-aware surrogate modeling for balancing approximation and sampling costs in multi-fidelity importance sampling and Bayesian inverse problems. SIAM/ASA Journal on Uncertainty Quantification, 2022. (accepted). |
| [41] | Towards context-aware learning for control: Balancing stability and model-learning error. In IEEE American Control Conference, 2022. |
| [42] | Breaking the Kolmogorov Barrier with Nonlinear Model Reduction. Notices of the American Mathematical Society, 69:725-733, 2022. |
| [43] | Accelerating the estimation of collisionless energetic particle confinement statistics in stellarators using multifidelity Monte Carlo. Nuclear Fusion, 2022. (accepted). |
| [44] | Data-driven low-fidelity models for multi-fidelity Monte Carlo sampling in plasma micro-turbulence analysis. Journal of Computational Physics, 2021. (accepted). |
| [45] | Multilevel Stein variational gradient descent with applications to Bayesian inverse problems. In Mathematical and Scientific Machine Learning (MSML) 2021, 2021. |
| [46] | An Extensible Benchmark Suite for Learning to Simulate Physical Systems. In NeurIPS 2021 Track Datasets and Benchmarks, 2021. (accepted). |
| [47] | Operator inference of non-Markovian terms for learning reduced models from partially observed state trajectories. Journal of Scientific Computing, 2021. (accepted). |
| [48] | Probabilistic error estimation for non-intrusive reduced models learned from data of systems governed by linear parabolic partial differential equations. ESAIM: Mathematical Modelling and Numerical Analysis (M2AN), 2021. (accepted). |
| [49] | Sampling low-dimensional Markovian dynamics for pre-asymptotically recovering reduced models from data with operator inference. SIAM Journal on Scientific Computing, 42:A3489-A3515, 2020. |
| [50] | Learning low-dimensional dynamical-system models from noisy frequency-response data with Loewner rational interpolation. In Realization and Model Reduction of Dynamical Systems: A Festschrift in Honor of the 70th Birthday of Thanos Antoulas, Springer, 2020. |
| [51] | Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms. Computer Methods in Applied Mechanics and Engineering, 372, 2020. |
| [52] | Stability of discrete empirical interpolation and gappy proper orthogonal decomposition with randomized and deterministic sampling points. SIAM Journal on Scientific Computing, 42:A2837-A2864, 2020. |
| [53] | Model reduction for transport-dominated problems via online adaptive bases and adaptive sampling. SIAM Journal on Scientific Computing, 42:A2803-A2836, 2020. |
| [54] | Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems. Physica D: Nonlinear Phenomena, Volume 406, 2020. |
| [55] | Quasi-optimal sampling to learn basis updates for online adaptive model reduction with adaptive empirical interpolation. In American Control Conference (ACC) 2020, IEEE, 2020. |
| [56] | A transport-based multifidelity preconditioner for Markov chain Monte Carlo. Advances in Computational Mathematics, 45:2321-2348, 2019. |
| [57] | Multifidelity Monte Carlo estimation with adaptive low-fidelity models. SIAM/ASA Journal on Uncertainty Quantification, 7:579-603, 2019. |
| [58] | Multifidelity probability estimation via fusion of estimators. Journal of Computational Physics, 392:385-402, 2019. |
| [59] | Projection-based model reduction: Formulations for physics-based machine learning. Computers & Fluids, 179:704-717, 2019. |
| [60] | Multifidelity preconditioning of the cross-entropy method for rare event simulation and failure probability estimation. SIAM/ASA Journal on Uncertainty Quantification, 6(2):737-761, 2018. |
| [61] | Convergence analysis of multifidelity Monte Carlo estimation. Numerische Mathematik, 139(3):683-707, 2018. |
| [62] | Multifidelity Monte Carlo Estimation of Variance and Sensitivity Indices. SIAM/ASA Journal on Uncertainty Quantification, 6(2):683-706, 2018. |
| [63] | Optimal Approximations of Coupling in Multidisciplinary Models. AIAA Journal, 56:2412-2428, 2018. |
| [64] | Geometric subspace updates with applications to online adaptive nonlinear model reduction. SIAM Journal on Matrix Analysis and Applications, 39(1):234-261, 2018. |
| [65] | Survey of multifidelity methods in uncertainty propagation, inference, and optimization. SIAM Review, 60(3):550-591, 2018. |
| [66] | Data-driven reduced model construction with time-domain Loewner models. SIAM Journal on Scientific Computing, 39(5):A2152-A2178, 2017. |
| [67] | Multifidelity Cross-Entropy Estimation of Conditional Value-at-Risk for Risk-Averse Design Optimization. In AIAA Scitech 2020 Forum, AIAA, 2020. |
| [68] | Multifidelity Monte Carlo estimation for large-scale uncertainty propagation. In 2018 AIAA Non-Deterministic Approaches Conference, AIAA, 2018. |
| [69] | Optimal Approximations of Coupling in Multidisciplinary Models. In 58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, AIAA, 2017. |
| [70] | Combining multiple surrogate models to accelerate failure probability estimation with expensive high-fidelity models. Journal of Computational Physics, 341:61-75, 2017. |
| [71] | Feedback Control for Systems with Uncertain Parameters Using Online-Adaptive Reduced Models. SIAM Journal on Applied Dynamical Systems, 16(3):1563-1586, 2017. |
| [72] | Optimal model management for multifidelity Monte Carlo estimation. SIAM Journal on Scientific Computing, 38(5):A3163-A3194, 2016. |
| [73] | Data-driven operator inference for nonintrusive projection-based model reduction. Computer Methods in Applied Mechanics and Engineering, 306:196-215, 2016. |
| [74] | Dynamic data-driven model reduction: Adapting reduced models from incomplete data. Advanced Modeling and Simulation in Engineering Sciences, 3(11), 2016. |
| [75] | Multifidelity Importance Sampling. Computer Methods in Applied Mechanics and Engineering, 300:490-509, 2016. |
| [76] | Online Adaptive Model Reduction for Nonlinear Systems via Low-Rank Updates. SIAM Journal on Scientific Computing, 37(4):A2123-A2150, 2015. |
| [77] | Detecting and Adapting to Parameter Changes for Reduced Models of Dynamic Data-driven Application Systems . In International Conference on Computational Science, Volume 51 of Procedia Computer Science, pages 2553-2562, Elsevier, 2015. |
| [78] | Reduced Models for Sparse Grid Discretizations of the Multi-Asset Black-Scholes Equation. Advances in Computational Mathematics, 41(5):1365-1389, 2015. |
| [79] | Dynamic Data-Driven Reduced-Order Models. Computer Methods in Applied Mechanics and Engineering, 291:21-41, 2015. |
| [80] | A Multigrid Method for Adaptive Sparse Grids. SIAM Journal on Scientific Computing, 37(5):S51-S70, 2015. |
| [81] | Parametric model order reduction by sparse-grid-based interpolation on matrix manifolds for multidimensional parameter spaces. In European Control Conference (ECC) 2014, IEEE, 2014. |
| [82] | Density Estimation with Adaptive Sparse Grids for Large Data Sets. In SIAM Data Mining 2014, SIAM, 2014. |
| [83] | Localized Discrete Empirical Interpolation Method. SIAM Journal on Scientific Computing, 36(1):A168-A192, 2014. |
| [84] | Selected Recent Applications of Sparse Grids. Numerical Mathematics: Theory, Methods and Applications, 8(1):47-77, 2014. |
| [85] | Classification with Probability Density Estimation on Sparse Grids. In Sparse Grids and Applications 2012, Volume 97 of Lecture Notes in Computational Science and Engineering, 2014. (accepted). |
| [86] | Image Segmentation with Adaptive Sparse Grids. In AI 2013: Advances in Artificial Intelligence, Volume 8272 of Lecture Notes in Computer Science Volume, pages 160-165, Springer, 2013. |
| [87] | Analysis of car crash simulation data with nonlinear machine learning methods. In International Conference on Computational Science, Volume 18 of Procedia Computer Science, pages 621-630, Elsevier, 2013. |
| [88] | Model Reduction with the Reduced Basis Method and Sparse Grids. In Sparse Grids and Applications 2011, Volume 88 of Lecture Notes in Computational Science and Engineering, pages 223-242, Springer, 2013. |
| [89] | Fast Insight into High-Dimensional Parametrized Simulation Data. In 11th International Conference on Machine Learning and Applications (ICMLA), pages 265-270, IEEE, 2012. |
| [90] | Clustering Based on Density Estimation with Sparse Grids. In KI 2012: Advances in Artificial Intelligence, Volume 7526 of Lecture Notes in Computer Science, pages 131-142, Springer, 2012. |
| [91] | Sparse Grid Classifiers as Base Learners for AdaBoost. In International Conference on High Performance Computing and Simulation (HPCS), pages 161-166, IEEE, 2012. |
| [92] | Semi-Coarsening in Space and Time for the Hierarchical Transformation Multigrid Method. In International Conference on Computational Science, Volume 9 of Procedia Computer Science, pages 2000-2003, Elsevier, 2012. |
| [93] | A Sparse-Grid-Based Out-of-Sample Extension for Dimensionality Reduction and Clustering with Laplacian Eigenmaps. In AI 2011: Advances in Artificial Intelligence, Volume 7106 of Lecture Notes in Computer Science, pages 112-121, Springer, 2011. |
| [94] | Spatially adaptive sparse grids for high-dimensional data-driven problems. Journal of Complexity, 26(5):508-522, 2010. |
Book chapters
| [1] | Neural Galerkin schemes for sequential-in-time solving of partial differential equations with deep networks. In Handbook of Numerical Analysis: Numerical Analysis Meets Machine Learning, pages 1-30, Elsevier, 2024. |
Talks
| [1] | Reduced Modeling of Chaotic, Turbulent, and Stochastic Systems via Population Dynamics. In SIAM Conference on Uncertainty Quantification, Minneapolis, MN, 2026. |
| [2] | Data-driven reduced modeling of chaotic, turbulent, and stochastic systems via population dynamics. In Data-driven methods for partial differential equations, Karlsruhe Institute of Technology, Germany, 2026. |
| [3] | DICE: Discrete inverse continuity equation for learning population dynamics. In Mechanical & Industrial Engineering Seminar, New Jersey Institute of Technology, Newark, NJ, 2026. |
| [4] | DICE: Discrete inverse continuity equation for learning population dynamics. In Workshop on industrial applications of numerical analysis and machine learning, Centrum Wiskunde & Informatica (CWI), Netherlands, 2025. |
| [5] | DICE: Discrete inverse continuity equation for learning population dynamics. In Mathematics Colloquium, Virginia Polytechnic Institute and State University, Blackburg, VA, 2025. |
| [6] | DICE: Discrete inverse continuity equation for learning population dynamics. In Institute for Mathematical and Statistical Innovation (IMSI), Workshop on Data Assimilation and Inverse Problems for Digital Twins, Chicago, IL, 2025. |
| [7] | DICE: Discrete inverse continuity equation for learning population dynamics. In 10th Workshop on High-Dimensional Approximation, Germany, 2025. |
| [8] | DICE: Discrete inverse continuity equation for learning population dynamics. In Wolfgang Pauli Institute, Plasma Kinetics Working Meeting, Austria, 2025. |
| [9] | DICE: Discrete inverse continuity equation for marginal trajectory matching. In Institute for Mathematical and Statistical Innovation, Statistical and Computational Challenges in Probabilistic Scientific Machine Learning, Chicago, IL, 2025. |
| [10] | Fast inference in generative modeling of time-dependent processes. In Department of Applied Mathematics seminar, Seattle, WA, 2025. |
| [11] | Fast inference in generative modeling of time-dependent processes. In Annual Meeting of European Mathematical Society activity group on Scientific Machine Learning, Milan, Italy, 2025. |
| [12] | Leveraging Nonlinear Latent Dynamics for Numerically Forecasting High-Dimensional Systems. In Energy High Performance Computing Conference, Houston, TX, 2025. |
| [13] | Parametric model reduction of stochastic systems via population dynamics. In Institute for Mathematics and its Applications, University of Minnesota, Minneapolis, MN, 2025. |
| [14] | Parametric model reduction of stochastic systems via population dynamics. In Computational Learning for Model Reduction, Institute for Computational and Experimental Research in Mathematics, Providence, RI, 2025. |
| [15] | Leveraging nonlinear latent dynamics of high-dimensional systems for data-driven predictions. In Distinguished Seminar in Computational Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 2024. |
| [16] | Parametric model reduction of stochastic systems via population dynamics. In SIAM Conference on Mathematics of Data Science, Atlanta, GA, 2024. |
| [17] | Continuous low-rank adaptation for reduced implicit neural modeling of parameterized partial differential equations. In Advanced Modeling & Simulation Research Laboratory, UTEP, online, 2024. |
| [18] | Continuous low-rank adaptation for reduced implicit neural modeling of parameterized partial differential equations. In Center for Mathematics and Artificial Intelligence, George Mason University, Fairfax, VA, 2024. |
| [19] | Sequential-in-time training of nonlinear parametrizations for solving time-dependent partial differential equations. In NSF Computational Mathematics PI Meeting, Seattle, WA, 2024. |
| [20] | Multilevel Stein variational gradient descent with applications to Bayesian inverse problems. In Seminar on Uncertainty Quantification, NASA Langley Research Center, Hampton, VA, 2024. |
| [21] | Continuous low-rank adaptation for reduced implicit neural modeling of parameterized partial differential equations. In Mathematical and Statistical Foundations of Digital Twins, Institute for Mathematical and Statistical Innovation, Chicago, IL, 2024. |
| [22] | Leveraging nonlinear latent dynamics for data-driven predictions. In Mathematical Sciences seminar, IBM, New York, NY, 2024. |
| [23] | Neural Galerkin schemes for model reduction of transport-dominated problems. In Numerical Analysis of Galerkin ROMs online seminar series, online, 2024. |
| [24] | Leveraging nonlinear latent dynamics for data-driven predictions. In Widely Applied Mathematics Seminar, Cambridge, MA, 2024. |
| [25] | Leveraging nonlinear latent dynamics for data-driven predictions. In Center for Approximation and Mathematical Data Analytics, College Town, TX, 2024. |
| [26] | Randomized sparse Neural Galerkin schemes for solving evolution equations with deep networks. In MORTech - International Workshop on Model Reduction Techniques, Paris, France, 2023. |
| [27] | Nonlinear model reduction with adaptive bases and adaptive sampling. In Applied Mathematics and Scientific Computing Seminar, Temple University, Philadelphia, PA, 2023. |
| [28] | Nonlinear model reduction with adaptive bases and adaptive sampling. In International Council for Industrial and Applied Mathematics (ICIAM) Congress, Tokyo, Japan, 2023. (online). |
| [29] | Nonlinear parametrizations for mitigating the Kolmogorov barrier in model reduction. In International Conference on Spectral and High Order Methods (ICOSAHOM), Seoul, South Korea, 2023. |
| [30] | Adaptivity in Reduced Order models. In Data-driven and Reduced Order Modeling for Multi-Scale Problems, Dayton, OH, 2023. |
| [31] | Context-aware controller inference. In Workshop on The mathematical and statistical foundation of future data-driven engineering, Cambridge, UK, 2023. |
| [32] | Coupling adaptive sampling and training with Neural Galerkin schemes for high-dimensional evolution equations. In Workshop on Scientific Machine Learning, Austin, TX, 2023. |
| [33] | Active sampling and Neural Galerkin schemes for high-dimensional evolution equations. In Numerical Analysis Seminar, University of Hong Kong, Hong Kong, Hong Kong, 2023. |
| [34] | Neural Galerkin Schemes for Evolution Equations. In SIAM Conference on Computational Science and Engineering, Amsterdam, Netherlands, 2023. |
| [35] | Multi-Fidelity Methods. In Simons Hour Talks, Simons Collaboration on Hidden Symmetries and Fusion Energy, Princeton, NJ, 2023. |
| [36] | Scientific machine learning with multi-fidelity methods and operator inference. In Siemens, Princeton, NJ, 2023. |
| [37] | Neural Galerkin Schemes for High-Dimensional Evolution Equations. In Computational and Applied Mathematics Colloquium, University of Chicago, Chicago, IL, 2022. |
| [38] | Neural Galerkin Schemes for High-Dimensional Evolution Equations. In Applied Mathematics and Computation Seminar, University of Massachusetts Amherst, online, 2022. |
| [39] | Neural Galerkin and Active Learning for High-Dimensional Evolution Equations. In SIAM Conference on Mathematics of Data Science, San Diego, CA, 2022. |
| [40] | Active learning for solving high-dimensional evolution equations. In 30th Birthday of Acta Numerica, Banach Centre, Poland, 2022. |
| [41] | Multifidelity uncertainty quantification. In AIAA Workshop on Multifidelity modeling in support of design and uncertainty quantification, AIAA Aviation, Chicago, IL, 2022. |
| [42] | Multilevel Stein variational gradient descent with applications to Bayesian inverse problems. In Erwin Schrödinger International Institute for Mathematics and Physics: Computational Uncertainty Quantification: Mathematical Foundations, Methodology & Data, Vienna, Austria, 2022. |
| [43] | Neural Galerkin schemes with active learning for high-dimensional evolution equations. In Data-driven Physical Simulations (DDPS) seminar, online, 2022. |
| [44] | Neural Galerkin for Solving Partial Differential Equations with Local Transport-Dominated Dynamics. In SIAM Conference on Uncertainty Quantification, Atlanta, GA, 2022. |
| [45] | Active learning for solving high-dimensional evolution equations. In Atmosphere Ocean Science Colloquium, New York University, New York, NY, 2022. |
| [46] | Neural Galerkin for Solving Partial Differential Equations with Local Transport-Dominated Dynamics. In SIAM Conference on Analysis of Partial Differential Equations, online, 2022. |
| [47] | Establishing trust in decisions made from data: Physics-informed machine-learning models with computable generalization bounds. In Mathematics of Soft Matter, Institute for Mathematical and Statistical Innovation, online, 2022. |
| [48] | Scientific machine learning for solving high-dimensional evolution equations. In Graduate Student and PostDoc Seminar, Courant Institute of Mathematical Sciences, New York University, New York, NY, 2022. |
| [49] | Scientific machine learning for high-dimensional evolution equations. In Computational and Applied Mathematics Colloquium, Pennsylvania State University, online, 2022. |
| [50] | Nonlinear model reduction for chemically reacting flows. In Panel on Data-Driven Methods for Chemically Reacting Flows, AIAA SciTech Forum, online, 2022. |
| [51] | Nonlinear model reduction for transport-dominated problems. In RAMSES: Reduced order models; Approximation theory; Machine learning; Surrogates, Emulators and Simulators, online, 2021. |
| [52] | Physics-informed machine learning for quickly simulating transport-dominated physical phenomena. In Data-Driven Methods for Science and Engineering Seminar, University of Washington, online, 2021. |
| [53] | Scientific machine learning with operator inference and re-projection. In Mathematics Colloquium, Swiss Distance University, online, 2021. |
| [54] | Establishing trust in decisions made from data: Certifying physics-informed models learned from data with computable generalization bounds. In IACM Conference on Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology, online, 2021. |
| [55] | Context-aware model reduction for uncertainty quantification. In SIAM Annual Meeting 2021, online, 2021. |
| [56] | Scientific machine learning with operator inference and re-projection. In ISC High Performance 2021, online, 2021. |
| [57] | Scientific machine learning with operator inference and re-projection. In Next Generation Simulation seminar series, Siemens AG, online, 2021. |
| [58] | Modeling nonlinear low-dimensional dynamics with deep networks. In Mathematical Modeling and Simulation Seminar, Courant Institute, online, 2021. |
| [59] | Scientific Machine Learning with Operator Inference and Re-Projection. In SIAM Conference on Computational Science and Engineering 2021, online, 2021. |
| [60] | Nonlinear model reduction for transport-dominated problems. In Applied Mathematics Seminar, Courant Institute, online, 2021. |
| [61] | Scientific machine learning with operator inference and re-projection. In Aerospace Computational Design Laboratory Seminar, Massachusetts Institute of Technology, Cambridge, MA, 2020. |
| [62] | Nonlinear model reduction for transport-dominated problems. In Numerical Analysis and PDE Seminar, University of Delaware, Newark, DE, 2020. |
| [63] | A biased introduction to projection-based model reduction. In Descriptors of Energy Landscapes Using Topological Data Analysis Seminar Series, online, 2020. |
| [64] | Quasi-optimal sampling to learn basis updates for online adaptive model reduction with adaptive empirical interpolation. In American Control Conference (ACC) 2020, Denver, CO, 2020. |
| [65] | Learning low-dimensional dynamical-system models from data via non-intrusive model reduction. In MAC-MIGS afternoon on Randomness and Data, Edinburgh, United Kingdom, 2020. |
| [66] | Context-aware learning of surrogate models for multi-fidelity computations. In Computational Uncertainty Quantification: Mathematical Foundations, Methodology & Data, Vienna, Austria, 2020. |
| [67] | Sampling low-dimensional Markovian dynamics for learning certified reduced models from data. In Mathematics of Reduced Order Models, Institute for Computational and Experimental Research in Mathematics, Providence, RI, 2020. |
| [68] | Learning dynamical-system models from data with time-domain Loewner, dynamic mode decomposition, and operator inference. In Model and dimension reduction in uncertain and dynamic systems, Institute for Computational and Experimental Research in Mathematics, Providence, RI, 2020. |
| [69] | Recovering certified reduced dynamical-system models from data with operator inference. In SIAM Minisymposium on Applications of Machine Learning to the Analysis of Nonlinear Dynamical Systems at Joint Mathematics Meetings 2020, Denver, CO, 2020. |
| [70] | Multifidelity Cross-Entropy Estimation of Conditional Value-at-Risk for Risk-Averse Design Optimization. In AIAA SciTech 2020, Orlando, FL, 2020. |
| [71] | Recovering (certified) reduced models from data with operator inference and time-domain Loewner. In European Numerical Mathematics and Advanced Applications Conference, Amsterdam, Netherlands, 2019. |
| [72] | Sampling Markovian dynamics for learning low-dimensional dynamical-system models from data. In Computational Science Initiative, Brookhaven National Laboratory, Brookhaven, NY, 2019. |
| [73] | Sampling Markovian dynamics for learning low-dimensional dynamical-system models from data. In Workshop on Uncertainty Quantification, Machine Learning & Bayesian Statistics in Scientific Computing, University of Heidelberg, Heidelberg, Germany, 2019. |
| [74] | Data generation and time-delay corrections for learning reduced models with operator inference. In Physics Informed Machine Learning Workshop, Seattle, WA, 2019. |
| [75] | Learning reduced dynamical-system models from data via operator inference and Loewner interpolation. In Oden Institute for Computational Engineering and Sciences (ICES), University of Texas at Austin, Austin, TX, 2019. |
| [76] | Dynamic Coupling of Full and Reduced Models via Randomized Online Basis Updates. In SIAM Computational Science and Engineering 2019, Spokane, WA, 2019. |
| [77] | Model reduction for transport-dominated problems via adaptive basis updates. In Applied Mathematics Colloquium, Columbia, New York, NY, 2018. |
| [78] | Learning Context-Aware Reduced Models for Multifidelity Computations. In School for Simulation and Data Sciences, RWTH Aachen, Aachen, Germany, 2018. |
| [79] | Context-Aware Model Reduction. In Computational Mathematics and Simulation Science Seminar, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, 2018. |
| [80] | Dynamic Coupling of Full and Reduced Models via Randomized Online Basis Updates. In Numerical Analysis and Scientific Computing Seminar, Courant Institute, New York, NY, 2018. |
| [81] | Data-Driven Multifidelity Methods for Monte Carlo Estimation. In Workshop on Big Data Meets Large-Scale Computing, Institute for Pure & Applied Mathematics (IPAM), Los Angeles, CA, 2018. |
| [82] | Learning context-aware surrogate models for multifidelity uncertainty quantification. In World Congress in Computational Mechanics, New York, NY, 2018. |
| [83] | Multifidelity methods and context-aware model reduction for Monte Carlo estimation and beyond. In Seminar Numerische Mathematik, Technical University Berlin, Berlin, Germany, 2018. |
| [84] | A Multifidelity Cross-Entropy Method for Rare Event Simulation. In SIAM Uncertainty Quantification 2018, Garden Grove, CA, 2018. |
| [85] | Data-Driven Multifidelity Methods for Monte Carlo Estimation. In Model Reduction of Parametrized Systems (MoRePaS) IV, Nantes, France, 2018. |
| [86] | Multifidelity Monte Carlo estimation with adaptive low-fidelity models. In Reducing dimensions and cost for UQ in complex systems, Isaac Newton Institute for Mathematical Sciences, Cambridge, UK, 2018. |
| [87] | Data-Driven Multifidelity Methods for Monte Carlo Estimation. In Engineering Physics Seminars and Colloquium, University of Wisconsin-Madison, Madison, USA, 2018. |
| [88] | Multifidelity Monte Carlo estimation for large-scale uncertainty propagation. In 2018 AIAA Non-Deterministic Approaches Conference (AIAA SciTech), Kissimmee, USA, 2018. |
| [89] | Multifidelity methods for rare event simulation. In European Numerical Mathematics and Advanced Applications (ENUMATH), Bergen, Norway, 2017. |
| [90] | Online adaptive discrete empirical interpolation for nonlinear model reduction. In European Numerical Mathematics and Advanced Applications (ENUMATH), Bergen, Norway, 2017. |
| [91] | Multifidelity methods for uncertainty propagation and rare event simulation. In QUIET 2017 - Quantification of Uncertainty: Improving Efficiency and Technology SISSA, Trieste, Italy, 2017. |
| [92] | Optimal low-rank updates for online adaptive model reduction with the discrete empirical interpolation method. In Householder Symposium XX on Numerical Linear Algebra, Blacksburg, USA, 2017. |
| [93] | Multifidelity Monte Carlo Methods for Rare Event Simulation. In MATRIX Workshop on Inverse Problems, Melbourne, Australia, 2017. |
| [94] | Data-driven reduced model construction with the time-domain Loewner framework and operator inference. In Colloquium, Department of Mathematics, Virginia Tech, Blacksburg, USA, 2017. |
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