Boris Kramer
Orcid: 0000-0002-3626-7925
According to our database1,
Boris Kramer
authored at least 40 papers
between 2012 and 2024.
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Bibliography
2024
Bayesian identification of nonseparable Hamiltonians with multiplicative noise using deep learning and reduced-order modeling.
CoRR, 2024
Gradient Preserving Operator Inference: Data-Driven Reduced-Order Models for Equations with Gradient Structure.
CoRR, 2024
2023
Multifidelity conditional value-at-risk estimation by dimensionally decomposed generalized polynomial chaos-Kriging.
Reliab. Eng. Syst. Saf., July, 2023
PLoS Comput. Biol., 2023
Predicting solar wind streams from the inner-heliosphere to Earth via shifted operator inference.
J. Comput. Phys., 2023
Anti-symmetric and Positivity Preserving Formulation of a Spectral Method for Vlasov-Poisson Equations.
CoRR, 2023
Global sensitivity analysis in the limited data setting with application to char combustion.
CoRR, 2023
Symplectic model reduction of Hamiltonian systems using data-driven quadratic manifolds.
CoRR, 2023
Exact and optimal quadratization of nonlinear finite-dimensional non-autonomous dynamical systems.
CoRR, 2023
2022
PLoS Comput. Biol., October, 2022
Bi-fidelity conditional-value-at-risk estimation by dimensionally decomposed generalized polynomial chaos expansion.
CoRR, 2022
Preserving Lagrangian structure in data-driven reduced-order modeling of large-scale mechanical systems.
CoRR, 2022
Bayesian Identification of Nonseparable Hamiltonian Systems Using Stochastic Dynamic Models.
Proceedings of the 61st IEEE Conference on Decision and Control, 2022
2021
SIAM J. Appl. Dyn. Syst., 2021
Balanced Reduced-Order Models for Iterative Nonlinear Control of Large-Scale Systems.
IEEE Control. Syst. Lett., 2021
Hamiltonian Operator Inference: Physics-preserving Learning of Reduced-order Models for Hamiltonian Systems.
CoRR, 2021
Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inference.
CoRR, 2021
2020
Reliab. Eng. Syst. Saf., 2020
SIAM/ASA J. Uncertain. Quantification, 2020
Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms.
CoRR, 2020
2019
J. Comput. Phys., 2019
Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems.
CoRR, 2019
Learning physics-based reduced-order models for a single-injector combustion process.
CoRR, 2019
2018
SIAM J. Sci. Comput., 2018
Multifidelity Preconditioning of the Cross-Entropy Method for Rare Event Simulation and Failure Probability Estimation.
SIAM/ASA J. Uncertain. Quantification, 2018
SIAM/ASA J. Uncertain. Quantification, 2018
Nonlinear Model Order Reduction via Lifting Transformations and Proper Orthogonal Decomposition.
CoRR, 2018
2017
Feedback Control for Systems with Uncertain Parameters Using Online-Adaptive Reduced Models.
SIAM J. Appl. Dyn. Syst., 2017
Sparse Sensing and DMD-Based Identification of Flow Regimes and Bifurcations in Complex Flows.
SIAM J. Appl. Dyn. Syst., 2017
Combining multiple surrogate models to accelerate failure probability estimation with expensive high-fidelity models.
J. Comput. Phys., 2017
Robust POD model stabilization for the 3D Boussinesq equations based on Lyapunov theory and extremum seeking.
Proceedings of the 2017 American Control Conference, 2017
2016
Robust Reduced-Order Model Stabilization for Partial Differential Equations Based on Lyapunov Theory and Extremum Seeking with Application to the 3D Boussinesq Equations.
CoRR, 2016
Proceedings of the 2016 American Control Conference, 2016
Learning-based reduced order model stabilization for partial differential equations: Application to the coupled Burgers' equation.
Proceedings of the 2016 American Control Conference, 2016
2015
Proceedings of the American Control Conference, 2015
2012
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012