Daniel Seidl

1.1k total citations
32 papers, 480 citations indexed

About

Daniel Seidl is a scholar working on Biomedical Engineering, Mechanics of Materials and Statistics, Probability and Uncertainty. According to data from OpenAlex, Daniel Seidl has authored 32 papers receiving a total of 480 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Biomedical Engineering, 6 papers in Mechanics of Materials and 6 papers in Statistics, Probability and Uncertainty. Recurrent topics in Daniel Seidl's work include Elasticity and Material Modeling (8 papers), Probabilistic and Robust Engineering Design (6 papers) and Advanced Multi-Objective Optimization Algorithms (4 papers). Daniel Seidl is often cited by papers focused on Elasticity and Material Modeling (8 papers), Probabilistic and Robust Engineering Design (6 papers) and Advanced Multi-Objective Optimization Algorithms (4 papers). Daniel Seidl collaborates with scholars based in United States, Germany and Israel. Daniel Seidl's co-authors include Hendrik Lehnert, Hendrik Ungefroren, Ralf Hass, Susanne Sebens, Assad A. Oberai, Phillip L. Reu, Katharina Mandel, Dirk Rades, Paul E. Barbone and Frank Gieseler and has published in prestigious journals such as PLoS ONE, The Journal of the Acoustical Society of America and Computer Methods in Applied Mechanics and Engineering.

In The Last Decade

Daniel Seidl

27 papers receiving 464 citations

Peers

Daniel Seidl

ONCOL

Didier Boucher France

Tiancheng Liu United States

Huaitao Wang China

Po Liu China

Se‐woon Choe South Korea

Yonghui Wang China

Matthias Rübner Germany

Jiajun Cai China

Haoran Zhao China

Jiwei Jiao China

Didier Boucher France

Daniel Seidl

135 ×0.9

ONCOL

467 ×3.4

150 ×1.6

35 ×0.5

104 ×1.6

Citations per year, relative to Daniel Seidl Daniel Seidl (= 1×) peers Didier Boucher

Countries citing papers authored by Daniel Seidl

Since Specialization

Citations

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

Fields of papers citing papers by Daniel Seidl

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Seidl

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Seidl. A scholar is included among the top collaborators of Daniel Seidl 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 Daniel Seidl. Daniel Seidl 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

Yan, Ruifeng, Daniel Seidl, Reese E. Jones, & Panayiotis Papadopoulos. (2025). A direct-adjoint approach for material point model calibration with application to plasticity. Computational Materials Science. 255. 113885–113885.

Jones, Elizabeth M. C., et al.. (2025). Identification Uncertainty in Inverse Material Model Parameter Determination: A Sensitivity‐Based Decision Process for Load Path Selection. Strain. 61(3).

Cohen, Ofer, et al.. (2025). Physics augmented machine learning discovery of composition-dependent constitutive laws for 3D printed digital materials. International Journal of Engineering Science. 217. 104381–104381. 1 indexed citations

Seidl, Daniel, et al.. (2025). A comparative study of calibration techniques for finite strain elastoplasticity: Numerically-exact sensitivities for FEMU and VFM. Computer Methods in Applied Mechanics and Engineering. 444. 118159–118159. 1 indexed citations

Jones, Elizabeth M. C., et al.. (2024). Digital image correlation and infrared thermography data for seven unique geometries of 304L stainless steel. Scientific Data. 11(1). 1101–1101. 3 indexed citations

Fuhg, Jan N., et al.. (2024). Polyconvex neural network models of thermoelasticity. Journal of the Mechanics and Physics of Solids. 192. 105837–105837. 12 indexed citations

Seidl, Daniel, et al.. (2024). Optimizing Variational Quantum Circuits Using Metaheuristic Strategies in Reinforcement Learning. 323–328. 1 indexed citations

Seidl, Daniel, et al.. (2023). Bayesian optimal experimental design for constitutive model calibration. International Journal of Mechanical Sciences. 265. 108881–108881. 3 indexed citations

Geraci, Gianluca, et al.. (2023). MULTILEVEL MONTE CARLO ESTIMATORS FOR DERIVATIVE-FREE OPTIMIZATION UNDER UNCERTAINTY. International Journal for Uncertainty Quantification. 14(3). 21–65. 2 indexed citations

10.

Debusschere, Bert, et al.. (2023). Machine Learning Surrogates of a Fuel Matrix Degradation Process Model for Performance Assessment of a Nuclear Waste Repository. Nuclear Technology. 209(9). 1295–1318. 3 indexed citations

11.

Jones, Elizabeth M. C., et al.. (2022). On the Importance of Direct-Levelling for Constitutive Material Model Calibration using Digital Image Correlation and Finite Element Model Updating. Experimental Mechanics. 63(3). 467–484. 17 indexed citations

12.

Debusschere, Bert, et al.. (2022). Machine Learning Surrogate Process Models for Efficient Performance Assessment of a Nuclear Waste Repository.. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information).

13.

Reu, Phillip L., et al.. (2022). Direct-Levelling Finite Element Analysis Data for Material Model Calibration using Digital Image Correlation and Finite Element Model Updating.. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1 indexed citations

14.

Seidl, Daniel, et al.. (2021). Calibration of elastoplastic constitutive model parameters from full‐field data with automatic differentiation‐based sensitivities. International Journal for Numerical Methods in Engineering. 123(1). 69–100. 14 indexed citations

15.

Seidl, Daniel, et al.. (2020). Correction to: Spatial DIC Errors due to Pattern-Induced Bias and Grey Level Discretization. Experimental Mechanics. 60(4). 573–573. 1 indexed citations

16.

Seidl, Daniel, et al.. (2019). Spatial DIC Errors due to Pattern-Induced Bias and Grey Level Discretization. Experimental Mechanics. 60(2). 249–263. 30 indexed citations

17.

Seidl, Daniel, et al.. (2014). Stiffness versus prestress relationship at subcellular length scale. Journal of Biomechanics. 47(12). 3222–3225. 4 indexed citations

18.

Mandel, Katharina, Daniel Seidl, Dirk Rades, et al.. (2013). Correction: Characterization of Spontaneous and TGF-β-Induced Cell Motility of Primary Human Normal and Neoplastic Mammary Cells In Vitro Using Novel Real-Time Technology. PLoS ONE. 8(4). 5 indexed citations

19.

Mandel, Katharina, Daniel Seidl, Dirk Rades, et al.. (2013). Characterization of Spontaneous and TGF-β-Induced Cell Motility of Primary Human Normal and Neoplastic Mammary Cells In Vitro Using Novel Real-Time Technology. PLoS ONE. 8(2). e56591–e56591. 36 indexed citations

20.

Ungefroren, Hendrik, Susanne Sebens, Daniel Seidl, Hendrik Lehnert, & Ralf Hass. (2011). Interaction of tumor cells with the microenvironment. Cell Communication and Signaling. 9(1). 18–18. 245 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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