Bernard Haasdonk

3.7k total citations
92 papers, 2.0k citations indexed

About

Bernard Haasdonk is a scholar working on Statistical and Nonlinear Physics, Computational Mechanics and Numerical Analysis. According to data from OpenAlex, Bernard Haasdonk has authored 92 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 56 papers in Statistical and Nonlinear Physics, 34 papers in Computational Mechanics and 29 papers in Numerical Analysis. Recurrent topics in Bernard Haasdonk's work include Model Reduction and Neural Networks (56 papers), Numerical methods for differential equations (27 papers) and Advanced Numerical Methods in Computational Mathematics (22 papers). Bernard Haasdonk is often cited by papers focused on Model Reduction and Neural Networks (56 papers), Numerical methods for differential equations (27 papers) and Advanced Numerical Methods in Computational Mathematics (22 papers). Bernard Haasdonk collaborates with scholars based in Germany, Italy and United States. Bernard Haasdonk's co-authors include Mario Ohlberger, H. Bürkhardt, Claus Bahlmann, Elżbieta Pękalska, Gabriele Santin, Daniel Keysers, Hans Burkhardt, Gianluigi Rozza, David Amsallem and D. C. Sorensen and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Pattern Analysis and Machine Intelligence and Bioresource Technology.

In The Last Decade

Bernard Haasdonk

88 papers receiving 1.9k citations

Peers

Bernard Haasdonk

SNP

SPU

CVPR

Anders Lindquist Sweden

Bassam Bamieh United States

Biswa Nath Datta United States

Alireza Doostan United States

A.C. Antoulas United States

S. Bittanti Italy

P. Dewilde Netherlands

Gilead Tadmor United States

A. Tesi Italy

Athanasios C. Antoulas United States

Anders Lindquist Sweden

Bernard Haasdonk

603 ×0.6

SNP

363 ×0.5

324 ×0.8

SPU

238 ×0.7

CVPR

420 ×1.2

Citations per year, relative to Bernard Haasdonk Bernard Haasdonk (= 1×) peers Anders Lindquist

Countries citing papers authored by Bernard Haasdonk

Since Specialization

Citations

This map shows the geographic impact of Bernard Haasdonk'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 Bernard Haasdonk with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bernard Haasdonk more than expected).

Fields of papers citing papers by Bernard Haasdonk

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bernard Haasdonk. 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 Bernard Haasdonk. The network helps show where Bernard Haasdonk may publish in the future.

Co-authorship network of co-authors of Bernard Haasdonk

This figure shows the co-authorship network connecting the top 25 collaborators of Bernard Haasdonk. A scholar is included among the top collaborators of Bernard Haasdonk 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 Bernard Haasdonk. Bernard Haasdonk is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Santin, Gabriele, et al.. (2025). Analysis of structured deep kernel networks. Journal of Computational and Applied Mathematics. 476. 116975–116975.

Fehr, Jörg, et al.. (2024). Improved a posteriori error bounds for reduced port-Hamiltonian systems. Advances in Computational Mathematics. 50(5).

Haasdonk, Bernard, et al.. (2024). Dictionary-based online-adaptive structure-preserving model order reduction for parametric Hamiltonian systems. Advances in Computational Mathematics. 50(1). 2 indexed citations

Hammer, Maria, et al.. (2024). A new method to design energy-conserving surrogate models for the coupled, nonlinear responses of intervertebral discs. Biomechanics and Modeling in Mechanobiology. 23(3). 757–780. 1 indexed citations

Haasdonk, Bernard, et al.. (2023). A novel model extended from the Bouguer-Lambert-Beer law can describe the non-linear absorbance of potassium dichromate solutions and microalgae suspensions. Frontiers in Bioengineering and Biotechnology. 11. 1116735–1116735. 15 indexed citations

Haasdonk, Bernard, et al.. (2023). A new Certified Hierarchical and Adaptive RB-ML-ROM Surrogate Model for Parametrized PDEs.

Schmid‐Staiger, Ulrike, et al.. (2023). Improving Determination of Pigment Contents in Microalgae Suspension with Absorption Spectroscopy: Light Scattering Effect and Bouguer–Lambert–Beer Law. Marine Drugs. 21(12). 619–619. 6 indexed citations

Syed, Tehreem, Ulrike Schmid‐Staiger, Bernard Haasdonk, et al.. (2023). Improving microalgae growth modeling of outdoor cultivation with light history data using machine learning models: A comparative study. Bioresource Technology. 390. 129882–129882. 22 indexed citations

Haasdonk, Bernard, et al.. (2022). Greedy sampling and approximation for realizing feedback control for high dimensional nonlinear systems. IFAC-PapersOnLine. 55(20). 325–330. 2 indexed citations

10.

Haasdonk, Bernard, et al.. (2022). Optimal Bases for Symplectic Model Order Reduction of Canonizable Linear Hamiltonian Systems. IFAC-PapersOnLine. 55(20). 463–468. 5 indexed citations

11.

Haasdonk, Bernard, et al.. (2020). Feedback control of parametrized PDEs via model order reduction and dynamic programming principle. IRIS Research product catalog (Sapienza University of Rome). 6 indexed citations

12.

Santin, Gabriele, et al.. (2018). Interpolation with uncoupled separable matrix-valued kernels. 11(3). 4 indexed citations

13.

Haasdonk, Bernard, et al.. (2016). A reduced basis Landweber method for nonlinear inverse problems. Inverse Problems. 32(3). 35001–35001. 13 indexed citations

14.

Amsallem, David, Charbel Farhat, & Bernard Haasdonk. (2015). Special Issue on Model Reduction. International Journal for Numerical Methods in Engineering. 102(5). 931–932. 7 indexed citations

15.

Haasdonk, Bernard, et al.. (2013). A Vectorial Kernel Orthogonal Greedy Algorithm. 6. 22 indexed citations

16.

Haasdonk, Bernard, et al.. (2012). Reduced Basis Model Reduction of Parametrized Two—Phase Flow in Porous Media. IFAC Proceedings Volumes. 45(2). 722–727. 6 indexed citations

17.

Haasdonk, Bernard. (2010). Effiziente und gesicherte Modellreduktion für parametrisierte dynamische Systeme (Efficient and Certified Model Reduction for Parametrized Dynamical Systems).. 58. 468–474. 1 indexed citations

18.

Pękalska, Elżbieta & Bernard Haasdonk. (2008). Kernel Discriminant Analysis for Positive Definite and Indefinite Kernels. IEEE Transactions on Pattern Analysis and Machine Intelligence. 31(6). 1017–1032. 67 indexed citations

19.

Peschke, Klaus, Bernard Haasdonk, Olaf Ronneberger, et al.. (2006). Using transformation knowledge for the classification of Raman spectra of biological samples. FreiDok plus (Universitätsbibliothek Freiburg). 288–293. 7 indexed citations

20.

Bahlmann, Claus, Bernard Haasdonk, & H. Bürkhardt. (2003). Online handwriting recognition with support vector machines - a kernel approach. 49–54. 200 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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