### Boris Krämer

I am broadly interested in ** computational methods and numerical analysis for control, optimization, design and uncertainty quantification of complex and large-scale systems **. I work on using reduced-order models in the context of multifidelity and data-driven modeling, optimization and control, uncertainty quantification, reliability-based design, and design under uncertainty. More information on my CV.

### Boris Krämer

Assistant Professor

Department of Mechanical and Aerospace Engineering

at the University of California San Diego

Jacobs Hall (EBU1) | Room 3313

9500 Gilman Drive | La Jolla | CA 92093-0411

### Affiliations

Center for Extreme Events Research (CEER)Center for Computational Mathematics (CCoM)

Graduate Program in Computational Science, Mathematics and Engineering

### News

09/30 - 10/04/2019: At the ENUMATH 2019 conference, I will present recent work on Lifting transformations and model reduction in MS31 on Monday September 30 at 1:30PM. I will also present Reduced order models for risk measure estimation in robust design at 11:05AM on Thursday October 3rd in MS18.

08/13/2019: Our paper "Learning physics-based reduced-order models for a single-injector combustion process" (with Renee Swischuk, Cheng Huang, and Karen Willcox) is now posted at arXiv:1908.03620 and Oden Institute Report 19-13.

07/29/2019: Our paper Balanced truncation model reduction for lifted nonlinear systems (with Karen Willcox) is now posted on arxiv, see arXiv:1907.12084.

07/28 - 08/01/2019: I'll attend the 15th U.S. National Congress on Computational Mechanics in Austin, TX and will be presenting Multifidelity Estimation of Risk Measures in Robust Design on July 31 from 3:40pm-4:00pm in MS#205 Multilevel/Multifidelity Strategies for Uncertainty Quantification.

Two papers that I co-authored are on the most-cited list of SIAM Journal of Uncertainty Quantification (accessed 06/17/2019), see Conditional-Value-at-Risk Estimation via Reduced-Order Models (with Matthias Heinkenschloss, Timur Takhtaganov, Karen Willcox) and Multifidelity Preconditioning of the Cross-Entropy Method for Rare Event Simulation and Failure Probability Estimation (with Benjamin Peherstorfer, Karen Willcox).

06/17/2019: Our conference paper Transform & Learn: A data-driven approach to nonlinear model reduction. (with Elizabeth Qian, Alexandre Marques and Karen Willcox) appeared in the proceedings of the AIAA Aviation 2019 Forum. Software for this publication available under https://github.com/elizqian/transform-and-learn and we have also made a more general operator inference code available at https://github.com/elizqian/operator-inference .

05/06/2019: Our paper Multifidelity probability estimation via fusion of estimators (with Alexandre Marques, Umberto Villa, Benjamin Peherstorfer and Karen Willcox) is now published at the Journal of Computational Physics, link.

04/23/2019: We posted a paper on Adaptive reduced-order model construction for conditional value-at-risk estimation (with Matthias Heinkenschloss and Timur Takhtaganov).

04/22/2019: Our paper Nonlinear Model Order Reduction via Lifting Transformations and Proper Orthogonal Decomposition (with Karen Willcox) appeared online at AIAA Journal, link.

01/29/2019: We posted a paper on Information reuse for importance sampling in reliability-based design optimization (with Anirban Chaudhuri and Karen Willcox).

12/04/2018: After 3.5 years of review, our patent finally got granted: US10145576B2: System and method for controlling operations of air-conditioning system.