Philipp Grohs

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Universität Wien

Austria

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Recent Grants

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Discretization of geometrically exact elasto-plastic Cosserat shells using geodesic finite elements

Open Date: 2014-01-01

Close Date: 2015-12-31

Grant: Close

Sparse Discretization of Kinetic Transport Problems on High-dimensional Phase Spaces

Open Date: 2013-04-01

Close Date: 2016-08-31

Grant: Close

Intrinsic Approximation of Geometric Data

Open Date: 2012-10-01

Close Date: 2015-09-30

Grant: Close

Stable Infinite Dimensional Phase Retrieval

Open Date:

Close Date:

Articles (17)

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2024

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2024

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2022

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2022

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2022

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2022

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2022

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2022

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2022

Collaborators (3)

Philipp Marquetand

Universität Wien

AUSTRIA

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Michael Scherbela

Universität Wien

AUSTRIA

View details

Rafael Reisenhofer

Universität Wien

AUSTRIA

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Philipp Grohs

Research Interests

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Recent Grants

Articles (17)

Stable Gabor Phase Retrieval in Gaussian Shift-Invariant Spaces via Biorthogonality

Towards a transferable fermionic neural wavefunction for molecules

Towards a transferable fermionic neural wavefunction for molecules

Non-uniqueness theory in sampled STFT phase retrieval

Injectivity of Gabor phase retrieval from lattice measurements

Space-time error estimates for deep neural network approximations for differential equations

Phaseless sampling on square-root lattices

Phase Transitions in Rate Distortion Theory and Deep Learning

Non-uniqueness theory in sampled STFT phase retrieval

Phase Transitions in Rate Distortion Theory and Deep Learning

Solving the electronic Schrödinger equation for multiple nuclear geometries with weight-sharing deep neural networks

Integral representations of shallow neural network with Rectified Power Unit activation function

Deep neural network approximations for solutions of PDEs based on Monte Carlo algorithms

Deep neural network approximation for high-dimensional elliptic PDEs with boundary conditions

Uniform error estimates for artificial neural network approximations for heat equations

Approximations with deep neural networks in Sobolev time-space

Deep neural network approximations for solutions of PDEs based on Monte Carlo algorithms

Collaborators (3)

Philipp Marquetand

Michael Scherbela

Rafael Reisenhofer

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