153 citations · 899 across the 45 of their papers we have counts for
62 papers
Phase retrieval in Fock space and perturbation of Liouville sets
Philipp Grohs, Lukas Liehr, Martin Rathmair
We study the determination of functions in Fock space from samples of their absolute value, known as the phase retrieval problem in Fock space. An important finding in this researc…
Variational Monte Carlo on a Budget -- Fine-tuning pre-trained Neural Wavefunctions
Michael Scherbela, Leon Gerard, Philipp Grohs
Obtaining accurate solutions to the Schrödinger equation is the key challenge in computational quantum chemistry. Deep-learning-based Variational Monte Carlo (DL-VMC) has recently…
FakET: Simulating Cryo-Electron Tomograms with Neural Style Transfer
Pavol Harar, Lukas Herrmann, Philipp Grohs +1
In cryo-electron microscopy, accurate particle localization and classification are imperative. Recent deep learning solutions, though successful, require extensive training data se…
Towards a Foundation Model for Neural Network Wavefunctions
Michael Scherbela, Leon Gerard, Philipp Grohs
Deep neural networks have become a highly accurate and powerful wavefunction ansatz in combination with variational Monte Carlo methods for solving the electronic Schrödinger equat…
From completeness of discrete translates to phaseless sampling of the short-time Fourier transform
Philipp Grohs, Lukas Liehr, Irina Shafkulovska
We study the uniqueness problem in short-time Fourier transform phase retrieval by exploring a connection to the completeness problem of discrete translates. Specifically, we prove…
Phaseless sampling on square-root lattices
Philipp Grohs, Lukas Liehr
Due to its appearance in a remarkably wide field of applications, such as audio processing and coherent diffraction imaging, the short-time Fourier transform (STFT) phase retrieval…