Boris Kramer

Assistant Professor

University of California, San Diego

United States

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Dr. Boris Kramer is an Assistant Professor at the University of California San Diego. His research areas include scalable computation for control-affine polynomial systems, risk-based design optimization in additive manufacturing, and systems biology modeling using Bayesian multimodel inference. He has authored several recent articles focusing on data-driven model order reduction, robust design optimization, and multifidelity distribution estimation.

Articles (20)

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2025

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2022

Collaborators (9)

E. Camporeale

UNITED STATES

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Jacquelien M. A. Scherpen

University of Groningen

NETHERLANDS

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ZHU WANG

University of South Carolina

UNITED STATES

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Serkan Gugercin

Professor

Virginia Tech

UNITED STATES

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Reza Vaziri

Professor

The University of British Columbia

CANADA

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Patrick Buchfink

University of Twente

NETHERLANDS

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Dongjin Lee

Assistant Professor

Hanyang University

SOUTH KOREA

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Karen Willcox

University of Texas at Austin

UNITED STATES

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Padmini Rangamani

University of California

UNITED STATES

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Boris Kramer

Research Interests

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About

Articles (20)

Increasing certainty in systems biology models using Bayesian multimodel inference

Scalable computation of input-normal/output-diagonal balanced realization for control-affine polynomial systems

Risk-based design optimization for powder bed fusion metal additive manufacturing

Conservative projection-based data-driven model order reduction of a fluid-kinetic spectral solver

Scalable Computation of $\mathcal {H}_\infty$ Energy Functions for Polynomial Control-Affine Systems

Robust design optimization with limited data for char combustion

Gradient preserving Operator Inference: Data-driven reduced-order models for equations with gradient structure

Scalable computation of energy functions for nonlinear balanced truncation

An Approximate Control Variates Approach to Multifidelity Distribution Estimation

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

Bayesian parameter estimation for the inclusion of uncertainty in progressive damage simulation of composites

Symplectic model reduction of Hamiltonian systems using data-driven quadratic manifolds

Bayesian Inference and Global Sensitivity Analysis for Ambient Solar Wind Prediction

Multifidelity conditional value-at-risk estimation by dimensionally decomposed generalized polynomial chaos-Kriging

Bi-fidelity conditional value-at-risk estimation by dimensionally decomposed generalized polynomial chaos expansion

Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inference

Hamiltonian operator inference: Physics-preserving learning of reduced-order models for canonical Hamiltonian systems

Interpolatory Methods for Model Reduction [Bookshelf]

Bayesian parameter estimation for dynamical models in systems biology

Learning state variables for physical systems

Collaborators (9)

E. Camporeale

Jacquelien M. A. Scherpen

ZHU WANG

Serkan Gugercin

Reza Vaziri

Patrick Buchfink

Dongjin Lee

Karen Willcox

Padmini Rangamani

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