Credit: UC San Diego Publications | Copyright Regents of the University of California

### Research Interests

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 can be found on my CV.

I am affiliated with the Center for Extreme Events Research (CEER) as well as the Center for Computational Mathematics (CCoM). Interested graduate students in the Graduate Program in Computational Science, Mathematics and Engineering can contact me to dicuss opportunities.

### Contact

Boris Krämer

Assistant Professor

Department of Mechanical and Aerospace Engineering

University of California San Diego

Jacobs Hall (EBU1) | Room 3113

9500 Gilman Drive | La Jolla | CA 92093-0411

- +1 (858) 246-5327
- bmkramer at ucsd dot edu
- https://www.linkedin.com/in/kramerboris
- Google Scholar: Papers and citations
- ResearchGate: Published research and discussions
- ORCiD: Persistant digital identifier
- Publons: Peer-reviewing profile

### News

**Sept. 2021:** The IACM Conference on Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology will be hosted here in San Diego from September 26-29, 2021, and I am excited to be on the local organizing committee. Stay tuned for updates.

**Sept. 2020:** With a great team of collaborators at MIT and the University of Michigan, we obtained an award for SWQU: Composable Next Generation Software Framework for Space Weather Data Assimilation and Uncertainty Quantification, where we will be integrating data-driven and projection-based model reduction for space weather applications, and use them in the context of uncertainty quantification. Refer to the UCSD news article Making space weather forecasts faster and better for more info.

**Jul. 2020:** Excited to receive the DoD Newton award to work with Prof. Melvin Leok on structure-preserving model reduction, see the news release: Mathematician, Engineer receive Newton Award for Transformative Ideas during COVID-19 Pandemic, and also the DoD announcement.

**Jul. 2020:** I am excited to partner with Confluency LLC, Chicago on an NSF Grant 2004275 SBIR Phase I: Human-Centered, Augmented Intelligence Software for Water and Wastewater. This Small Business Innovation Research (SBIR) Phase I project will develop methods for combining multi-fidelity simulation models and data-driven models to support decision-making for both long-term planning needs and real-time operational decision support for water and wastewater systems.

**Apr. 2020:** Over the last two years, we have developed new methods to learn nonlinear dynamical systems from data as part of the Air Force Center of Excellence Multi-Fidelity Modeling of Rocket Combustion Dynamics. In collaboration with Renee Swischuk, Elizabeth Qian, Benjamin Peherstorfer, Cheng Huang and Karen Willcox, we published those results in Learning Physics-Based Reduced-Order Models for a Single-Injector Combustion Process and Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems (see the Publications page for dowload and Codes for the source code and data.) Some recent media coverage:

**Mar. 2020:** Due to the COVID-19 outbreak, several upcoming conferences that I would have attended got cancelled: The SIAM Conference on Mathematics of Data Science, the SIAM Annual Meeting 2020, the Southern California Applied Mathematics Symposium and the 2020 Center for Extreme Events Research (CEER) Research Summit at UC San Diego. In the absense of those venues, I'll be posting more research updates here and feel free to reach out.

**Mar. 2020:** Our paper Learning Physics-Based Reduced-Order Models for a Single-Injector Combustion Process (with Renee Swischuk, Cheng Huang, Karen Willcox) finally appeared in AIAA Journal. This paper learns a data-driven reduced-order model from simulated combustion data of over 300,000 degrees of freedom.

**Feb. 2020:** We posted our paper Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms (with Peter Benner, Pawan Goyal, Benjamin Peherstorfer and Karen Willcox) as an arxiv preprint.

**Feb. 2020:** Our paper Adaptive reduced-order model construction for conditional value-at-risk estimation (with Matthias Heinkenschloss and Timur Takhtaganov) got accepted to SIAM/ASA Journal on Uncertainty Quantification.

**Feb. 2020:** Our paper Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems (with Elizabeth Qian, Benjamin Peherstorfer, Karen Willcox) appeared in Physica D: Nonlinear Phenomena.

** Feb. 2020:** I attended the ICERM Workshop on Mathematics of Reduced Order Models at Brown University and presented work on Operator Inference for non-polynomial systems & control.

**Jan. 2020:** Our paper Information reuse for importance sampling in reliability-based design optimization got accepted in Reliability Engineering & System Saftey, and was mentioned in the Aerospace America: 2019 Year in Review.

**Jan. 2020:** Our paper Learning physics-based reduced-order models for a single-injector combustion process (with Renee Swischuk, Cheng Huang, Karen Willcox) appeared in the conference proceedings of the AIAA SciTech 2020 Forum.

**Jan. 2020:** Our paper Risk-based design optimization via probability of failure, conditional value-at-risk, and buffered probability of failure (with Anirban Chaudhuri and Matthew Norton) appeared in the conference proceedings of the AIAA SciTech 2020 Forum.

**Nov. 2019:** I attended the Workshop on Feedback control and gave a talk on "LQR control for systems with uncertain parameters via onlineāadaptive reduced models." The workshop was part of an excellent Special Semester on Optimization at the Johann-Radon Institute for Computational and Applied Mathematics (RICAM), JKU Linz.

**Oct. 2019:** At the ENUMATH 2019 conference, I presented Lifting transformations and model reduction as well as Reduced order models for risk measure estimation in robust design..

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

**Jul. 2019:** I attended the 15th U.S. National Congress on Computational Mechanics in Austin, TX presented 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.

**Jun. 2019:** 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).

**Jun. 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 .

**May 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.

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

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