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Philipp Grohs publication distribution in Mathematics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mathematics in 2026. The highlighted bar marks where Philipp Grohs sits on this spectrum.

42–46 publications: 3 scientists 47–51 publications: 5 scientists 52–56 publications: 7 scientists 57–61 publications: 20 scientists 62–66 publications: 14 scientists 67–71 publications: 25 scientists 72–76 publications: 19 scientists 77–81 publications: 35 scientists 82–86 publications: 50 scientists 87–91 publications: 60 scientists 92–96 publications: 86 scientists 97–101 publications: 84 scientists 102–106 publications: 83 scientists 107–111 publications: 90 scientists 112–116 publications: 99 scientists 117–121 publications: 90 scientists 122–126 publications: 91 scientists 127–131 publications: 109 scientists 132–136 publications: 110 scientists 137–141 publications: 98 scientists 142–146 publications: 112 scientists 147–151 publications: 102 scientists 152–156 publications: 88 scientists 157–161 publications: 106 scientists 162–166 publications: 83 scientists 167–171 publications: 102 scientists 172–176 publications: 77 scientists 177–181 publications: 81 scientists 182–186 publications: 78 scientists 187–191 publications: 71 scientists 192–196 publications: 92 scientists 197–201 publications: 64 scientists 202–206 publications: 69 scientists 207–211 publications: 64 scientists 212–216 publications: 62 scientists 217–221 publications: 58 scientists 222–226 publications: 53 scientists 227–231 publications: 50 scientists 232–236 publications: 46 scientists 237–241 publications: 46 scientists 242–246 publications: 46 scientists 247–251 publications: 43 scientists 252–256 publications: 29 scientists 257–261 publications: 45 scientists 262–266 publications: 30 scientists 267–271 publications: 33 scientists 272–276 publications: 34 scientists 277–281 publications: 30 scientists 282–286 publications: 31 scientists 287–291 publications: 21 scientists 292–296 publications: 34 scientists 297–301 publications: 26 scientists 302–306 publications: 10 scientists 307–311 publications: 17 scientists 312–316 publications: 23 scientists 317–321 publications: 13 scientists 322–326 publications: 16 scientists 327–331 publications: 26 scientists 332–336 publications: 13 scientists 337–341 publications: 13 scientists 342–346 publications: 16 scientists 347–351 publications: 17 scientists 352–356 publications: 12 scientists 357–361 publications: 18 scientists 362–366 publications: 18 scientists 367–371 publications: 9 scientists 372–376 publications: 11 scientists 377–381 publications: 8 scientists 382–386 publications: 8 scientists 387–391 publications: 9 scientists 392–396 publications: 9 scientists 397–401 publications: 8 scientists 402–406 publications: 11 scientists 407–411 publications: 6 scientists 412–416 publications: 6 scientists 417–421 publications: 9 scientists 422–426 publications: 8 scientists 427–431 publications: 5 scientists 432–436 publications: 8 scientists 437–441 publications: 8 scientists 442–446 publications: 4 scientists 447–451 publications: 4 scientists 452–456 publications: 4 scientists 457–461 publications: 2 scientists 462–466 publications: 2 scientists 467–471 publications: 4 scientists 472–476 publications: 3 scientists 477–481 publications: 3 scientists 482–486 publications: 6 scientists 487–491 publications: 3 scientists 492–496 publications: 5 scientists 497–501 publications: 5 scientists 502–506 publications: 1 scientists 507–511 publications: 6 scientists 512–516 publications: 4 scientists 517–521 publications: 1 scientists 522–526 publications: 3 scientists 527–531 publications: 1 scientists 532–536 publications: 4 scientists 537+ publications: 100 scientists
42 publications 537+

The last bar groups every scientist with 537 publications or more.

Philipp Grohs D-index placement in Mathematics in 2026

The chart shows the D-index (discipline H-index) distribution of Mathematics scientists ranked by Research.com in 2026. The highlighted bar marks where Philipp Grohs sits on this spectrum.

30 D-Index: 174 scientists 31 D-Index: 151 scientists 32 D-Index: 174 scientists 33 D-Index: 117 scientists 34 D-Index: 136 scientists 35 D-Index: 127 scientists 36 D-Index: 145 scientists 37 D-Index: 153 scientists 38 D-Index: 150 scientists 39 D-Index: 150 scientists 40 D-Index: 138 scientists 41 D-Index: 136 scientists 42 D-Index: 93 scientists 43 D-Index: 108 scientists 44 D-Index: 115 scientists 45 D-Index: 112 scientists 46 D-Index: 103 scientists 47 D-Index: 75 scientists 48 D-Index: 59 scientists 49 D-Index: 67 scientists 50 D-Index: 60 scientists 51 D-Index: 57 scientists 52 D-Index: 59 scientists 53 D-Index: 62 scientists 54 D-Index: 60 scientists 55 D-Index: 50 scientists 56 D-Index: 42 scientists 57 D-Index: 54 scientists 58 D-Index: 50 scientists 59 D-Index: 42 scientists 60 D-Index: 41 scientists 61 D-Index: 35 scientists 62 D-Index: 40 scientists 63 D-Index: 21 scientists 64 D-Index: 31 scientists 65 D-Index: 27 scientists 66 D-Index: 29 scientists 67 D-Index: 19 scientists 68 D-Index: 25 scientists 69 D-Index: 17 scientists 70 D-Index: 18 scientists 71 D-Index: 12 scientists 72 D-Index: 14 scientists 73 D-Index: 13 scientists 74 D-Index: 18 scientists 75 D-Index: 9 scientists 76 D-Index: 11 scientists 77 D-Index: 10 scientists 78 D-Index: 9 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 10 scientists 82 D-Index: 5 scientists 83 D-Index: 5 scientists 84 D-Index: 13 scientists 85 D-Index: 6 scientists 86+ D-Index: 99 scientists
30 D-Index 86+

The last bar groups every scientist with 86 D-Index or more.

Overview

Philipp Grohs is affiliated with the University of Vienna in Austria. Their research primarily spans the fields of Computer Science and Physics and Astronomy, with a significant focus on several subfields and topics connecting deep learning, neural networks, and applied mathematics.

Grohs's main subfields of study include:

  • Artificial Intelligence
  • Statistical and Nonlinear Physics
  • Geophysics
  • Radiation
  • Computer Vision and Pattern Recognition

Their work covers a variety of research topics, notably:

  • Model Reduction and Neural Networks
  • Neural Networks and Applications
  • Seismic Imaging and Inversion Techniques
  • Advanced X-ray Imaging Techniques
  • Machine Learning in Materials Science
  • Stochastic processes and financial applications
  • Mathematical Analysis and Transform Methods

Grohs has contributed to numerous publications throughout their career. Some recent representative papers include:

  • A Proof that Artificial Neural Networks Overcome the Curse of Dimensionality in the Numerical Approximation of Black-Scholes Partial Differential Equations (2023, Memoirs of the American Mathematical Society)
  • DNN Expression Rate Analysis of High-Dimensional PDEs: Application to Option Pricing (2021, Constructive Approximation)
  • Deep Neural Network Approximation Theory (2021, IEEE Transactions on Information Theory)
  • Group Testing for SARS-CoV-2 Allows for Up to 10-Fold Efficiency Increase Across Realistic Scenarios and Testing Strategies (2021, Frontiers in Public Health)
  • Space-time error estimates for deep neural network approximations for differential equations (2023, Advances in Computational Mathematics)

In addition to articles, Grohs has published books, notably through Cambridge University Press, including Mathematical Aspects of Deep Learning (2022).

Their publication record shows frequent appearances in venues such as:

  • arXiv (Cornell University)
  • Constructive Approximation
  • Foundations of Computational Mathematics
  • Advances in Computational Mathematics
  • IMA Journal of Numerical Analysis

Common collaborators in Grohs's research include:

  • Lukas Liehr
  • Julius Berner
  • Arnulf Jentzen
  • Michael Scherbela
  • Leon Gerard

Best Publications

  • Optimal Approximation with Sparsely Connected Deep Neural Networks

    Helmut Bölcskei;Philipp Grohs;Gitta Kutyniok;Philipp Petersen

  • A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations

    Philipp Grohs;Fabian Hornung;Arnulf Jentzen;Philippe von Wurstemberger

  • Analysis of the generalization error: Empirical risk minimization over deep artificial neural networks overcomes the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations

    Julius Berner;Philipp Grohs;Arnulf Jentzen

  • Deep Neural Network Approximation Theory

    Dennis Elbrachter;Dmytro Perekrestenko;Philipp Grohs;Helmut Bolcskei

  • Solving stochastic differential equations and Kolmogorov equations by means of deep learning.

    Christian Beck;Sebastian Becker;Philipp Grohs;Nor Jaafari

  • DNN Expression Rate Analysis of High-Dimensional PDEs: Application to Option Pricing

    Dennis Elbrächter;Philipp Grohs;Philipp Grohs;Arnulf Jentzen;Arnulf Jentzen;Christoph Schwab

  • Laguerre minimal surfaces, isotropic geometry and linear elasticity

    Helmut Pottmann;Philipp Grohs;Niloy J. Mitra

  • Parabolic Molecules

    Philipp Grohs;Gitta Kutyniok

  • Solving the Kolmogorov PDE by Means of Deep Learning

    Christian Beck;Sebastian Becker;Philipp Grohs;Nor Jaafari

  • The Modern Mathematics of Deep Learning

    Julius Berner;Philipp Grohs;Gitta Kutyniok;Philipp Petersen

  • Phase Retrieval: Uniqueness and Stability

    Philipp Grohs;Sarah Koppensteiner;Martin Rathmair

  • Continuous shearlet frames and resolution of the wavefront set

    Philipp Grohs

  • ε-subgradient algorithms for locally lipschitz functions on Riemannian manifolds

    P. Grohs;S. Hosseini

  • Stable Phase Retrieval in Infinite Dimensions

    Rima Alaifari;Ingrid Daubechies;Philipp Grohs;Rujie Yin

  • Phase Retrieval In The General Setting Of Continuous Frames For Banach Spaces

    Rima Alaifari;Philipp Grohs

  • Stable Gabor Phase Retrieval and Spectral Clustering

    Philipp Grohs;Martin Rathmair

  • Smoothness Properties of Lie Group Subdivision Schemes

    J. Wallner;E. Nava Yazdani;P. Grohs

  • Nonsmooth trust region algorithms for locally Lipschitz functions on Riemannian manifolds

    P. Grohs;S. Hosseini

  • A General Proximity Analysis of Nonlinear Subdivision Schemes

    Philipp Grohs

  • Optimal A Priori Discretization Error Bounds for Geodesic Finite Elements

    Philipp Grohs;Hanne Hardering;Oliver Sander

Frequent Co-Authors

Arnulf Jentzen
Arnulf Jentzen Chinese University of Hong Kong, Shenzhen
Gitta Kutyniok
Gitta Kutyniok Ludwig-Maximilians-Universität München
Ingrid Daubechies
Ingrid Daubechies Duke University
Demetrio Labate
Demetrio Labate University of Houston
Stephan Dahlke
Stephan Dahlke Philipp University of Marburg
Ralf Hiptmair
Ralf Hiptmair ETH Zurich
Afra M. Wohlschläger
Afra M. Wohlschläger Technical University of Munich

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