ProfessorsProfessors of University of California, San DiegoBoris Kramer

Boris Kramer

University of California, San Diego

H-index: 19

North America-United States

About Boris Kramer

Boris Kramer, With an exceptional h-index of 19 and a recent h-index of 19 (since 2020), a distinguished researcher at University of California, San Diego, specializes in the field of Model Reduction, Uncertainty Quantification, Control, System Identification.

His recent articles reflect a diverse array of research interests and contributions to the field:

Lagrangian operator inference enhanced with structure-preserving machine learning for nonintrusive model reduction of mechanical systems

Exact and Optimal Quadratization of Nonlinear Finite-Dimensional Nonautonomous Dynamical Systems

Bayesian identification of nonseparable Hamiltonians with multiplicative noise using deep learning and reduced-order modeling

Gradient Preserving Operator Inference: Data-Driven Reduced-Order Models for Equations with Gradient Structure

Scalable computation of energy functions for nonlinear balanced truncation

Learning nonlinear reduced models from data with operator inference

Preserving Lagrangian structure in data-driven reduced-order modeling of large-scale dynamical systems

Global sensitivity analysis in the limited data setting with application to char combustion

Boris Kramer Information

University	University of California, San Diego
Position	___
Citations(all)	1296
Citations(since 2020)	1190
Cited By	441
hIndex(all)	19
hIndex(since 2020)	19
i10Index(all)	29
i10Index(since 2020)	24
Email	Access Email
University Profile Page	University of California, San Diego
Google Scholar	View Google Scholar Profile

Boris Kramer Skills & Research Interests

Model Reduction

Uncertainty Quantification

Control

System Identification

Top articles of Boris Kramer

Title	Journal	Author(s)	Publication Date
Lagrangian operator inference enhanced with structure-preserving machine learning for nonintrusive model reduction of mechanical systems	Computer Methods in Applied Mechanics and Engineering	Harsh Sharma David A Najera-Flores Michael D Todd Boris Kramer	2024/4/1
Exact and Optimal Quadratization of Nonlinear Finite-Dimensional Nonautonomous Dynamical Systems	SIAM Journal on Applied Dynamical Systems	Andrey Bychkov Opal Issan Gleb Pogudin Boris Kramer	2024/3/31
Bayesian identification of nonseparable Hamiltonians with multiplicative noise using deep learning and reduced-order modeling	arXiv preprint arXiv:2401.12476	Nicholas Galioto Harsh Sharma Boris Kramer Alex Arkady Gorodetsky	2024/1/23
Gradient Preserving Operator Inference: Data-Driven Reduced-Order Models for Equations with Gradient Structure	arXiv preprint arXiv:2401.12138	Yuwei Geng Jasdeep Singh Lili Ju Boris Kramer Zhu Wang	2024/1/22
Scalable computation of energy functions for nonlinear balanced truncation	Computer Methods in Applied Mechanics and Engineering	Boris Kramer Serkan Gugercin Jeff Borggaard Linus Balicki	2024/7/1
Learning nonlinear reduced models from data with operator inference		Boris Kramer Benjamin Peherstorfer Karen E Willcox	2024/1/19
Preserving Lagrangian structure in data-driven reduced-order modeling of large-scale dynamical systems	Physica D: Nonlinear Phenomena	Harsh Sharma Boris Kramer	2024/6/1
Global sensitivity analysis in the limited data setting with application to char combustion	arXiv preprint arXiv:2307.07486	Dongjin Lee Elle Lavichant Boris Kramer	2023/7/14
Anti-symmetric and Positivity Preserving Formulation of a Spectral Method for Vlasov-Poisson Equations	arXiv preprint arXiv:2312.05439	Opal Issan Oleksandr Koshkarov Federico D Halpern Boris Kramer Gian Luca Delzanno	2023/12/9
Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inference	Computer Methods in Applied Mechanics and Engineering	Nihar Sawant Boris Kramer Benjamin Peherstorfer	2023/2/1
Bayesian parameter estimation for the inclusion of uncertainty in progressive damage simulation of composites	Composite Structures	Johannes Reiner Nathaniel Linden Reza Vaziri Navid Zobeiry Boris Kramer	2023/10/1
Predicting solar wind streams from the inner-heliosphere to earth via shifted operator inference	Journal of Computational Physics	Opal Issan Boris Kramer	2023/1/15
Multifidelity conditional value-at-risk estimation by dimensionally decomposed generalized polynomial chaos-Kriging	Reliability Engineering & System Safety	Dongjin Lee Boris Kramer	2023/7/1
Full-Body Optimal Control of a Swimming Soft Robot Enabled by Data-Driven Model Reduction	Southern California Robotics Symposium	Iman Adibnazari Harsh Sharma Jacobo Cervera Torralba Boris Kramer Michael T Tolley	2023
An approximate control variates approach to multifidelity distribution estimation	arXiv preprint arXiv:2303.06422	Ruijian Han Boris Kramer Dongjin Lee Akil Narayan Yiming Xu	2023/3/11
Bayesian Inference and Global Sensitivity Analysis for Ambient Solar Wind Prediction	Space Weather	Opal Issan Pete Riley Enrico Camporeale Boris Kramer	2023/9/11
Nonlinear Balanced Truncation: Part 2--Model Reduction on Manifolds	arXiv preprint arXiv:2302.02036	Boris Kramer Serkan Gugercin Jeff Borggaard	2023/2/4
Symplectic model reduction of Hamiltonian systems using data-driven quadratic manifolds	Computer Methods in Applied Mechanics and Engineering	Harsh Sharma Hongliang Mu Patrick Buchfink Rudy Geelen Silke Glas ...	2023/12/1
Bi-fidelity conditional value-at-risk estimation by dimensionally decomposed generalized polynomial chaos expansion	Structural and Multidisciplinary Optimization	Dongjin Lee Boris Kramer	2023/2
Certifiable risk-based engineering design optimization	AIAA Journal	Anirban Chaudhuri Boris Kramer Matthew Norton Johannes O Royset Karen Willcox	2022/2