Boris Krämer

2.4k total citations
77 papers, 1.6k citations indexed

About

Boris Krämer is a scholar working on Statistical and Nonlinear Physics, Statistics, Probability and Uncertainty and Mechanical Engineering. According to data from OpenAlex, Boris Krämer has authored 77 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Statistical and Nonlinear Physics, 22 papers in Statistics, Probability and Uncertainty and 21 papers in Mechanical Engineering. Recurrent topics in Boris Krämer's work include Model Reduction and Neural Networks (32 papers), Probabilistic and Robust Engineering Design (22 papers) and Numerical methods for differential equations (11 papers). Boris Krämer is often cited by papers focused on Model Reduction and Neural Networks (32 papers), Probabilistic and Robust Engineering Design (22 papers) and Numerical methods for differential equations (11 papers). Boris Krämer collaborates with scholars based in United States, Japan and Germany. Boris Krämer's co-authors include Karen Willcox, B. F. von Turkovich, Benjamin Peherstorfer, Nam P. Suh, Elizabeth Qian, H. Kazerooni, Zhu Wang, Mouhacine Benosman, Padmini Rangamani and Serkan Gugercin and has published in prestigious journals such as Nature Communications, IEEE Transactions on Automatic Control and Journal of Computational Physics.

In The Last Decade

Boris Krämer

67 papers receiving 1.5k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Boris Krämer United States 21 687 496 331 261 260 77 1.6k
David Ryckelynck France 17 313 0.5× 681 1.4× 414 1.3× 477 1.8× 131 0.5× 80 1.4k
Somdatta Goswami United States 18 481 0.7× 733 1.5× 304 0.9× 1.0k 3.9× 187 0.7× 34 2.6k
Emmanuelle Abisset‐Chavanne France 16 289 0.4× 339 0.7× 172 0.5× 481 1.8× 238 0.9× 77 1.2k
Hongwei Guo China 15 458 0.7× 423 0.9× 111 0.3× 1.0k 3.9× 226 0.9× 60 2.4k
S.P. Lim Singapore 24 755 1.1× 327 0.7× 206 0.6× 1.0k 3.9× 584 2.2× 58 2.6k
Francisco Chinesta France 15 178 0.3× 681 1.4× 392 1.2× 560 2.1× 209 0.8× 41 1.5k
S. Clénet France 19 406 0.6× 353 0.7× 245 0.7× 127 0.5× 53 0.2× 154 1.4k
José Vicente Aguado France 16 220 0.3× 404 0.8× 223 0.7× 259 1.0× 226 0.9× 31 979
Ramin Bostanabad United States 18 585 0.9× 313 0.6× 304 0.9× 789 3.0× 245 0.9× 44 2.0k
Philip Avery United States 21 176 0.3× 579 1.2× 364 1.1× 314 1.2× 129 0.5× 50 1.3k

Countries citing papers authored by Boris Krämer

Since Specialization
Citations

This map shows the geographic impact of Boris Krämer's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Boris Krämer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Boris Krämer more than expected).

Fields of papers citing papers by Boris Krämer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Boris Krämer. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Boris Krämer. The network helps show where Boris Krämer may publish in the future.

Co-authorship network of co-authors of Boris Krämer

This figure shows the co-authorship network connecting the top 25 collaborators of Boris Krämer. A scholar is included among the top collaborators of Boris Krämer based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Boris Krämer. Boris Krämer is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Zhang, Jin, et al.. (2025). Systems modeling and uncertainty quantification of AMP-activated protein kinase signaling. npj Systems Biology and Applications. 11(1). 113–113.
2.
Ju, Lili, et al.. (2025). Data-driven reduced-order models for port-Hamiltonian systems with operator inference. Computer Methods in Applied Mechanics and Engineering. 442. 118042–118042. 1 indexed citations
3.
Ju, Lili, et al.. (2024). Gradient preserving Operator Inference: Data-driven reduced-order models for equations with gradient structure. Computer Methods in Applied Mechanics and Engineering. 427. 117033–117033. 4 indexed citations
4.
Krämer, Boris, et al.. (2024). Preserving Lagrangian structure in data-driven reduced-order modeling of large-scale dynamical systems. Physica D Nonlinear Phenomena. 462. 134128–134128. 11 indexed citations
5.
Lee, Dongjin, et al.. (2024). Global sensitivity analysis with limited data via sparsity-promoting D-MORPH regression: Application to char combustion. Journal of Computational Physics. 511. 113116–113116. 1 indexed citations
6.
Todd, Michael D., et al.. (2024). Lagrangian operator inference enhanced with structure-preserving machine learning for nonintrusive model reduction of mechanical systems. Computer Methods in Applied Mechanics and Engineering. 423. 116865–116865. 7 indexed citations
7.
Halpern, Federico David, et al.. (2024). Anti-symmetric and positivity preserving formulation of a spectral method for Vlasov-Poisson equations. Journal of Computational Physics. 514. 113263–113263. 3 indexed citations
8.
Krämer, Boris, et al.. (2024). Scalable Computation of $\mathcal {H}_\infty$ Energy Functions for Polynomial Control-Affine Systems. IEEE Transactions on Automatic Control. 70(5). 3088–3100.
9.
Tolley, Michael T., et al.. (2024). Data-driven Model Reduction for Soft Robots via Lagrangian Operator Inference. IFAC-PapersOnLine. 58(17). 91–96.
10.
Krämer, Boris, et al.. (2024). Computing Solutions to the Polynomial-Polynomial Regulator Problem*. 2689–2696. 1 indexed citations
11.
Geelen, Rudy, et al.. (2023). Symplectic model reduction of Hamiltonian systems using data-driven quadratic manifolds. Computer Methods in Applied Mechanics and Engineering. 417. 116402–116402. 18 indexed citations
12.
Riley, Pete, et al.. (2023). Bayesian Inference and Global Sensitivity Analysis for Ambient Solar Wind Prediction. Space Weather. 21(9). 9 indexed citations
13.
Lee, Dongjin & Boris Krämer. (2023). Multifidelity conditional value-at-risk estimation by dimensionally decomposed generalized polynomial chaos-Kriging. Reliability Engineering & System Safety. 235. 109208–109208. 3 indexed citations
14.
Benner, Peter, Pawan Goyal, Boris Krämer, Benjamin Peherstorfer, & Karen Willcox. (2020). Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms. Computer Methods in Applied Mechanics and Engineering. 372. 113433–113433. 70 indexed citations
15.
Krämer, Boris, et al.. (2019). Multifidelity probability estimation via fusion of estimators. Journal of Computational Physics. 392. 385–402. 21 indexed citations
16.
Grover, Piyush, Petros T. Boufounos, Saleh Nabi, Mouhacine Benosman, & Boris Krämer. (2017). Sparse Sensing and DMD-Based Identification of Flow Regimes and Bifurcations in Complex Flows. SIAM Journal on Control and Optimization. 2 indexed citations
17.
Krämer, Boris, Benjamin Peherstorfer, & Karen Willcox. (2017). Feedback Control for Systems with Uncertain Parameters Using Online-Adaptive Reduced Models. SIAM Journal on Applied Dynamical Systems. 16(3). 1563–1586. 17 indexed citations
18.
Burns, John A. & Boris Krämer. (2015). Full flux models for optimization and control of heat exchangers. 577–582. 12 indexed citations
19.
Fenske, G.R., et al.. (1988). Characterization of coating wear phenomena in nitride- and carbide-coated tool inserts. Surface and Coatings Technology. 36(3-4). 791–800. 23 indexed citations
20.
Krämer, Boris & B. F. von Turkovich. (1986). A Comprehensive Tool Wear Model. CIRP Annals. 35(1). 67–70. 62 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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