Selected preprints and submitted articles
| [1] | First-Order Trajectory Matching: Fast Ensemble Predictions of Chaotic, Turbulent, Stochastic Systems. arXiv, 2606.11138, 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. |
Journal publications
Conference proceedings
| [1] | Leveraging Gauge Freedom for Learning Non-Gradient Population Dynamics of Stochastic Systems. International Conference on Machine Learning (ICML), 2026. |
| [2] | Stochastic Lifting for Generating Trajectories of Stochastic Physical Systems. International Conference on Machine Learning (ICML), 2026. |
| [3] | 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). |
| [4] | Hankel Singular Value Regularization for Highly Compressible State Space Models. NeurIPS, 2025. |
| [5] | Parametric model reduction of mean-field and stochastic systems via higher-order action matching. NeurIPS, 2024. |
| [6] | CoLoRA: Continuous low-rank adaptation for reduced implicit neural modeling of parameterized partial differential equations. International Conference on Machine Learning (ICML), 2024. |
| [7] | Randomized Sparse Neural Galerkin Schemes for Solving Evolution Equations with Deep Networks. NeurIPS 2023 (spotlight). |
| [8] | Multi-fidelity covariance estimation in the log-Euclidean geometry. International Conference on Machine Learning (ICML), 2023. |
| [9] | 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. |
| [10] | Towards context-aware learning for control: Balancing stability and model-learning error. In IEEE American Control Conference, 2022. |
| [11] | Multilevel Stein variational gradient descent with applications to Bayesian inverse problems. In Mathematical and Scientific Machine Learning (MSML) 2021, 2021. |
| [12] | An Extensible Benchmark Suite for Learning to Simulate Physical Systems. In NeurIPS 2021 Track Datasets and Benchmarks, 2021. (accepted). |
| [13] | 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. |
| [14] | Quasi-optimal sampling to learn basis updates for online adaptive model reduction with adaptive empirical interpolation. In American Control Conference (ACC) 2020, IEEE, 2020. |
| [15] | Multifidelity Cross-Entropy Estimation of Conditional Value-at-Risk for Risk-Averse Design Optimization. In AIAA Scitech 2020 Forum, AIAA, 2020. |
| [16] | Multifidelity Monte Carlo estimation for large-scale uncertainty propagation. In 2018 AIAA Non-Deterministic Approaches Conference, AIAA, 2018. |
| [17] | Optimal Approximations of Coupling in Multidisciplinary Models. In 58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, AIAA, 2017. |
| [18] | 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. |
| [19] | Parametric model order reduction by sparse-grid-based interpolation on matrix manifolds for multidimensional parameter spaces. In European Control Conference (ECC) 2014, IEEE, 2014. |
| [20] | Density Estimation with Adaptive Sparse Grids for Large Data Sets. In SIAM Data Mining 2014, SIAM, 2014. |
| [21] | 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). |
| [22] | 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. |
| [23] | 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. |
| [24] | 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. |
| [25] | Fast Insight into High-Dimensional Parametrized Simulation Data. In 11th International Conference on Machine Learning and Applications (ICMLA), pages 265-270, IEEE, 2012. |
| [26] | 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. |
| [27] | Sparse Grid Classifiers as Base Learners for AdaBoost. In International Conference on High Performance Computing and Simulation (HPCS), pages 161-166, IEEE, 2012. |
| [28] | 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. |
| [29] | 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. |
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. |
| [95] | Multifidelity Methods for Uncertainty Propagation and Rare Event Simulation. In Workshop on Data-Driven Modeling and Uncertainty Quantification (UQPM), Austin, USA, 2017. |
| [96] | Multifidelity Monte Carlo Methods with Optimally-Adapted Surrogate Models. In SIAM Computational Science and Engineering 2017, Atlanta, USA, 2017. |
| [97] | Optimal sampling in multifidelity Monte Carlo estimation for efficient uncertainty propagation. In SILO Seminar Wisconsin Institute for Discovery, Madison, USA, 2017. |
| [98] | Optimal sampling in multifidelity Monte Carlo estimation for efficient uncertainty propagation. In Applied and Computational Mathematics Seminar Department of Mathematics, University of Wisconsin-Madison, Madison, USA, 2016. |
| [99] | Safe and Efficient Data-Driven Model Reduction for Critical Engineering Applications. In Next Generation Mobility Modeling and Simulation, Novi, USA, 2016. |
| [100] | Data-Driven Methods for Nonintrusive Model Reduction. In SIAM Annual Meeting 2016, Boston, USA, 2016. |
| [101] | Multifidelity Methods for Uncertainty Quantification. In Workshop on Data to Decisions in Aerospace Engineering, Auckland, New Zealand, 2016. |
| [102] | Multifidelity Methods for Uncertainty Quantification. In SIAM Uncertainty Quantification 2016, Lausanne, Switzerland, 2016. |
| [103] | Multifidelity Monte Carlo estimation with multiple surrogate models. In Copper Mountain conference on iterative methods, Copper Mountain, USA, 2016. |
| [104] | Multifidelity methods for uncertainty quantification. In Third International Workshop on Model Reduction for Parametrized Systems (MoRePaS III) SISSA, Trieste, Italy, 2015. |
| [105] | Online adaptive model reduction with dynamic models and sparse sampling. In European Numerical Mathematics and Advanced Applications (ENUMATH) Middle East Technical University, Ankara, Turkey, 2015. |
| [106] | Multifidelity Monte Carlo. In 6th Workshop on High-Dimensional Approximation University of Bonn, Bonn, Germany, 2015. |
| [107] | Detecting and Adapting to Parameter Changes for Reduced Models of Dynamic Data-driven Application Systems. In International Conference on Computational Science Reykjavík University, Reykjavík, Iceland, 2015. |
| [108] | Online Adaptive Model Reduction. In SIAM Conference on Computational Science and Engineering 2015 SIAM, Salt Lake City, USA, 2015. |
| [109] | Nonlinear model reduction through online adaptivity and dynamic models. In Scientific Computing Colloquium TUM, Munich, Germany, 2014. |
| [110] | Online Adaptive Model Reduction for Nonlinear Systems. In SIAM MIT Chapter 2014 MIT, Boston, USA, 2014. |
| [111] | Sparse grid density estimation with data independent quantities. In Sparse Grids and Applications 2014 SimTech, Stuttgart, Germany, 2014. |
| [112] | Density Estimation with Adaptive Sparse Grids for Large Datasets. In SIAM Data Mining 2014 SIAM, Philadelphia, USA, 2014. |
| [113] | Density Estimation with Adaptive Sparse Grids. In SIAM Uncertainty Quantification 2014 SIAM, Savannah, USA, 2014. |
| [114] | Localized model order reduction with machine learning methods. In SIAM and MIT CCE series Center for Computational Engineering, MIT, MIT, Boston, USA, 2014. |
| [115] | Localized Discrete Empirical Interpolation Method. In ACDL Seminars Department of Aeronautics and Astronautics, MIT, Department of Aeronautics and Astronautics, MIT, Boston, USA, 2014. |
| [116] | Localized DEIM based on feature extraction. In Model Reduction and Approximation for Complex Systems 2013 Institut für Informatik, Technische Universität München, Centre International de Rencontres Mathematiques, Marseille, France, 2013. |
| [117] | Tackling higher dimensionalities with sparse grids. In ACM/FEF 2013, San Diego, USA, 2013. |
| [118] | Density Estimation for Large Datasets with Sparse Grids. In SIAM Conference on Computational Science and Engineering Institut für Informatik, Technische Universität München, Boston, USA, 2013. |
| [119] | Dünne Gitter: Konstruktion und Anwendung optimaler Diskretisierungen. In NUMET 2013 Lehrstuhl für Strömungsmechanik (LSTM)Institut für Informatik, Technische Universität München, Lehrstuhl für Strömungsmechanik (LSTM), Universität Erlangen-Nürnberg, Germany, 2013. |
| [120] | Reduced Order Models with LDEIM for Parametrized PDEs with Nonlinear Terms. In Angewandte Analysis und Numerische Simulation Institut für Informatik, Technische Universität München, Universität Stuttgart, Germany, 2013. |
| [121] | Localized Discrete Empirical Interpolation Method. In Second International Workshop on Model Reduction for Parametrized Systems (MoRePaS II) Institut für Informatik, Technische Universität München, Schloss Reisensburg, Günzburg, Germany, 2012. |
| [122] | Clustering Based on Density Estimation with Sparse Grids. In KI 2012: Advances in Artificial Intelligence Institut für Informatik, Technische Universität München, Saarbrücken, Germany, 2012. |
| [123] | A Sparse-Grid-Based Out-of-Sample Extension for Dimensionality Reduction and Clustering with Laplacian Eigenmaps. In SGA 2012 Institut für Informatik, Technische Universität München, Munich, Germany, 2012. |
| [124] | A multigrid method for PDEs on spatially adaptive sparse grids. In 28th GAMM-Seminar on Analysis and Numerical Methods in Higher Dimensions Institut für Informatik, Technische Universität München, Leipzig, Germany, 2012. |
| [125] | Clustering of Truck-Data with Sparse Grids. In Project Meeting BMBF SIMDATA-NL Institut für Informatik, Technische Universität München, Fraunhofer SCAI, Bonn, Germany, 2011. |
| [126] | A multigrid method for PDEs on spatially adaptive sparse grids. In 4th Workshop on High-Dimensional Approximation Fakultät für Informatik, Technische Universität München, Bonn, Germany, 2011. |
| [127] | Reduced Basis Methods and Sparse Grids. In HIM - Workshop on Sparse Grids and Applications Fakultät für Informatik, Technische Universität München, Bonn, Germany, 2011. |
| [128] | Hierarchical Transformation Multigrid. In Ferienakademie Fakultät für Informatik, Technische Universität München, Durnholz, Italy, 2010. |
| [129] | Introduction to Reduced Basis Methods. In Ferienakademie Fakultät für Informatik, Technische Universität München, Durnholz, Italy, 2010. |