Philipp Grohs

2.3k total citations
86 papers, 850 citations indexed

About

Philipp Grohs is a scholar working on Computational Mechanics, Applied Mathematics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Philipp Grohs has authored 86 papers receiving a total of 850 indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Computational Mechanics, 24 papers in Applied Mathematics and 23 papers in Computer Vision and Pattern Recognition. Recurrent topics in Philipp Grohs's work include Advanced Numerical Analysis Techniques (24 papers), Image and Signal Denoising Methods (20 papers) and Mathematical Analysis and Transform Methods (18 papers). Philipp Grohs is often cited by papers focused on Advanced Numerical Analysis Techniques (24 papers), Image and Signal Denoising Methods (20 papers) and Mathematical Analysis and Transform Methods (18 papers). Philipp Grohs collaborates with scholars based in Austria, Switzerland and Germany. Philipp Grohs's co-authors include Arnulf Jentzen, Gitta Kutyniok, Christoph Schwab, Helmut Pottmann, Johannes Wallner, Niloy J. Mitra, Johannes Wallner, Ingrid Daubechies, Julius Berner and Oliver Sander and has published in prestigious journals such as Nature Communications, PLoS ONE and IEEE Transactions on Information Theory.

In The Last Decade

Philipp Grohs

78 papers receiving 794 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Philipp Grohs Austria 18 342 196 174 150 122 86 850
Ben Adcock Canada 18 562 1.6× 323 1.6× 295 1.7× 79 0.5× 114 0.9× 72 1.4k
Nicolas Boumal United States 17 495 1.4× 228 1.2× 64 0.4× 67 0.4× 177 1.5× 39 1.2k
Manfred Tasche Germany 17 238 0.7× 308 1.6× 234 1.3× 50 0.3× 184 1.5× 67 923
Afonso S. Bandeira United States 16 295 0.9× 287 1.5× 68 0.4× 74 0.5× 120 1.0× 44 1.0k
Anders C. Hansen United Kingdom 17 405 1.2× 268 1.4× 211 1.2× 61 0.4× 115 0.9× 39 1.2k
Peter Binev United States 16 560 1.6× 98 0.5× 34 0.2× 201 1.3× 281 2.3× 25 1.2k
Frank de Hoog Australia 20 217 0.6× 63 0.3× 141 0.8× 37 0.2× 153 1.3× 56 964
Folkmar Bornemann Germany 23 620 1.8× 167 0.9× 119 0.7× 196 1.3× 367 3.0× 60 1.7k
Rémi Vaillancourt Canada 19 144 0.4× 147 0.8× 476 2.7× 229 1.5× 175 1.4× 113 1.5k
John Strain United States 18 753 2.2× 237 1.2× 75 0.4× 101 0.7× 136 1.1× 34 1.4k

Countries citing papers authored by Philipp Grohs

Since Specialization
Citations

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

Fields of papers citing papers by Philipp Grohs

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Philipp Grohs

This figure shows the co-authorship network connecting the top 25 collaborators of Philipp Grohs. A scholar is included among the top collaborators of Philipp Grohs 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 Philipp Grohs. Philipp Grohs 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.
Sutterud, Halvard, et al.. (2025). Transferable neural wavefunctions for solids. Nature Computational Science. 5(12). 1147–1157.
2.
Grohs, Philipp, et al.. (2025). Phase retrieval in Fock space and perturbation of Liouville sets. Revista Matemática Iberoamericana. 41(3). 969–1008. 1 indexed citations
3.
Grohs, Philipp, et al.. (2024). Towards a transferable fermionic neural wavefunction for molecules. Nature Communications. 15(1). 120–120. 17 indexed citations
4.
Grohs, Philipp & Felix Voigtlaender. (2023). Proof of the Theory-to-Practice Gap in Deep Learning via Sampling Complexity bounds for Neural Network Approximation Spaces. Foundations of Computational Mathematics. 24(4). 1085–1143. 12 indexed citations
5.
Grohs, Philipp, et al.. (2023). Lower bounds for artificial neural network approximations: A proof that shallow neural networks fail to overcome the curse of dimensionality. Journal of Complexity. 77. 101746–101746. 5 indexed citations
6.
Grohs, Philipp, et al.. (2023). Space-time error estimates for deep neural network approximations for differential equations. Advances in Computational Mathematics. 49(1). 11 indexed citations
7.
Grohs, Philipp, et al.. (2023). Stable Gabor Phase Retrieval in Gaussian Shift-Invariant Spaces via Biorthogonality. Constructive Approximation. 59(1). 61–111. 5 indexed citations
8.
Grohs, Philipp, et al.. (2022). Mathematical Aspects of Deep Learning. Cambridge University Press eBooks. 46 indexed citations
9.
Grohs, Philipp, et al.. (2022). Deep neural network approximations for solutions of PDEs based on Monte Carlo algorithms. Partial Differential Equations and Applications. 3(4). 13 indexed citations
10.
Grohs, Philipp, et al.. (2022). Integral representations of shallow neural network with rectified power unit activation function. Neural Networks. 155. 536–550. 4 indexed citations
11.
Fischer, David S., et al.. (2021). Group Testing for SARS-CoV-2 Allows for Up to 10-Fold Efficiency Increase Across Realistic Scenarios and Testing Strategies. Frontiers in Public Health. 9. 583377–583377. 33 indexed citations
12.
13.
Berner, Julius, et al.. (2019). How degenerate is the parametrization of neural networks with the ReLU activation function. arXiv (Cornell University). 32. 7788–7799. 4 indexed citations
14.
Dahlke, Stephan, Filippo De Mari, Philipp Grohs, & Demetrio Labate. (2015). Harmonic and applied analysis : from groups to signals. CERN Document Server (European Organization for Nuclear Research). 3 indexed citations
15.
Grohs, Philipp, et al.. (2015). Optimal adaptive ridgelet schemes for linear advection equations. Applied and Computational Harmonic Analysis. 41(3). 768–814.
16.
Grohs, Philipp, et al.. (2014). Total Variation Regularization by Iteratively Reweighted Least Squares on Hadamard Spaces and the Sphere. 6 indexed citations
17.
Grohs, Philipp. (2012). Intrinsic localization of anisotropic frames. Applied and Computational Harmonic Analysis. 35(2). 264–283. 4 indexed citations
18.
Grohs, Philipp & Christoph Schwab. (2011). Sparse twisted tensor frame discretization of parametric transport operators. Kagoshima Kenritsu Tanki Daigaku Chiiki Kenkyūjo kenkyū nenpō. 1 indexed citations
19.
Grohs, Philipp & Johannes Wallner. (2009). Interpolatory wavelets for manifold-valued data. Applied and Computational Harmonic Analysis. 27(3). 325–333. 17 indexed citations
20.
Grohs, Philipp. (2009). Approximation order from stability for nonlinear subdivision schemes. Journal of Approximation Theory. 162(5). 1085–1094. 8 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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