Publications

Showing entries 21 - 40 out of 134
Redistributor: Transforming Empirical Data Distributions. / Harar, Pavol; Elbrächter, Dennis; Dörfler, Monika et al.
arXiv, 2022.

Publications: Working paper › Preprint

Phaseless sampling on square-root lattices. / Liehr, Lukas; Grohs, Philipp.
2022.

Publications: Working paper › Preprint

Deep neural network approximations for solutions of PDEs based on Monte Carlo algorithms. / Grohs, Philipp; Jentzen, Arnulf (Corresponding author); Salimova , Diyora .
In: SN Partial Differential Equations and Applications, Vol. 3, No. 4, 45, 08.2022.

Publications: Contribution to journal › Article › Peer Reviewed

Deep neural network approximation for high-dimensional elliptic PDEs with boundary conditions. / Grohs, Philipp; Herrmann, Lukas (Corresponding author).
In: IMA Journal of Numerical Analysis, Vol. 42, No. 3, drab031, 22.07.2022, p. 2055-2082.

Publications: Contribution to journal › Article › Peer Reviewed

Multi-window STFT phase retrieval: lattice uniqueness. / Liehr, Lukas; Grohs, Philipp; Rathmair, Martin.
arXiv.org, 2022.

Publications: Working paper › Preprint

Uniform error estimates for artificial neural network approximations for heat equations. / Gonon, Lukas; Grohs, Philipp; Jentzen, Arnulf et al.
In: IMA Journal of Numerical Analysis, Vol. 42, No. 3, drab027, 07.2022, p. 1991-2054.

Publications: Contribution to journal › Article › Peer Reviewed

Solving the electronic Schrödinger equation for multiple nuclear geometries with weight-sharing deep neural networks. / Scherbela, Michael; Reisenhofer, Rafael (Corresponding author); Gerard, Leon et al.
In: Nature Computational Science, Vol. 2, No. 5, 19.05.2022, p. 331–341.

Publications: Contribution to journal › Article › Peer Reviewed

Approximations with deep neural networks in Sobolev time-space. / Abdeljawad, Ahmed (Corresponding author); Grohs, Philipp.
In: Analysis and Applications, Vol. 20, No. 3, 10.05.2022, p. 499 -541.

Publications: Contribution to journal › Article › Peer Reviewed

On foundational discretization barriers in STFT phase retrieval. / Liehr, Lukas; Grohs, Philipp.
In: Journal of Fourier Analysis and Applications, Vol. 28, 39, 04.2022.

Publications: Contribution to journal › Article › Peer Reviewed

Quantification of Kuramoto Coupling Between Intrinsic Brain Networks Applied to fMRI Data in Major Depressive Disorder. / Bauer, Lena; Hirsch, Fabian ; Jones, Corey et al.
In: Frontiers in Computational Neuroscience, Vol. 16, 729556, 03.03.2022.

Publications: Contribution to journal › Article › Peer Reviewed

Assessing the heterogeneity in the transmission of infectious diseases from time series of epidemiological data. / Schneckenreiter, Günter ; Herrmann, Lukas; Reisenhofer, Rafael et al.
medRxiv, 2022.

Publications: Working paper › Preprint

DNN Expression Rate Analysis of High-dimensional PDEs: Application to Option Pricing. / Elbrächter, Dennis (Corresponding author); Grohs, Philipp; Jentzen, Arnulf et al.
In: Constructive Approximation, Vol. 55, No. 1, 02.2022, p. 3-71.

Publications: Contribution to journal › Article › Peer Reviewed

Die Moderne Mathematik des Tiefen Lernens. / Berner, Julius; Grohs, Philipp; Kutyniok, Gitta et al.
In: Mitteilungen der Deutschen Mathematiker-Vereinigung, Vol. 29, No. 4, 2022, p. 191-197.

Publications: Contribution to journal › Article › Peer Reviewed

Gold-standard solutions to the Schrödinger equation using deep learning: How much physics do we need? / Gerard, Leon; Scherbela, Michael; Marquetand, Philipp et al.
2022. Paper presented at Thirty-sixth Conference on Neural Information Processing Systems, New Orleans, United States.

Publications: Contribution to conference › Paper › Peer Reviewed

Lower bounds for artificial neural network approximations: A proof that shallow neural networks fail to overcome the curse of dimensionality. / Grohs, Philipp; Ibragimov, Shokhrukh ; Jentzen, Arnulf et al.
In: Journal of Complexity, 2022.

Publications: Contribution to journal › Article › Peer Reviewed

Stable Gabor phase retrieval for multivariate functions. / Grohs, Philipp; Rathmair, Martin.
In: Journal of the European Mathematical Society, Vol. 24, No. 5, 2022, p. 1593-1615.

Publications: Contribution to journal › Article › Peer Reviewed

RICAM, the Johann Radon Institute for Computational and Applied Mathematics. / Grohs, Philipp; Kritzer, Peter; Kunisch, Karl et al.
In: EMS magazine, No. 122, 12.2021, p. 46-51.

Publications: Contribution to journal › Article

Group Testing for SARS-CoV-2 Allows for Up to 10-Fold Efficiency Increase Across Realistic Scenarios and Testing Strategies. / Verdun, Claudio M.; Fuchs, Tim; Harár, Pavol et al.
In: Frontiers in Public Health, Vol. 9, 583377, 18.08.2021, p. 1205.

Publications: Contribution to journal › Article › Peer Reviewed

Deep Neural Network Approximation Theory. / Elbrächter, Dennis; Perekrestenko, Dmytro ; Grohs, Philipp et al.
In: IEEE TRANSACTIONS ON INFORMATION THEORY, Vol. 67, No. 5, 9363169, 05.2021, p. 2581-2623.

Publications: Contribution to journal › Article › Peer Reviewed

Gabor Phase Retrieval is Severely Ill-Posed. / Alaifari, Rima; Grohs, Philipp.
In: Applied and Computational Harmonic Analysis, Vol. 50, 01.2021, p. 401-419.

Publications: Contribution to journal › Article › Peer Reviewed

Showing entries 21 - 40 out of 134