653 citations
- University of ViennaAT117 papers
- TU WienAT57 papers
- Vienna Center for Quantum Science and TechnologyAT42 papers
- Centre National de la Recherche ScientifiqueFR23 papers
- Institute of MathematicsPL17 papers
- ETH ZurichCH12 papers
- Universität für Weiterbildung KremsAT9 papers
- University of OxfordGB8 papers
- Institute for Theoretical PhysicsCH7 papers
- Laboratoire Paul PainlevéFR7 papers
- Ludwig-Maximilians-Universität MünchenDE7 papers
- University of FreiburgDE7 papers
7 papers · 1 filter
Emergent Pauli blocking in a weakly interacting Bose gas
Federica Cataldini, Frederik Møller, Mohammadamin Tajik +6
The relationship between many-body interactions and dimensionality is integral to numerous emergent quantum phenomena. A striking example is the Bose gas, which upon confinement to…
Fermionization via cavity-assisted infinite-range interactions
Paolo Molignini, Camille Lévêque, Hans Kessler +3
We study a one-dimensional array of bosons with infinite-range interactions mediated by a laser-driven dissipative optical cavity. The cavity-mediated infinite-range interactions o…
Magnetostatics and micromagnetics with physics informed neural networks
Alexander Kovacs, Lukas Exl, Alexander Kornell +8
Partial differential equations and variational problems can be solved with physics informed neural networks (PINNs). The unknown field is approximated with neural networks. Minimiz…
Stellar evolution models with entropy-calibrated mixing-length parameter: application to red giants
Federico Spada, Pierre Demarque, Friedrich Kupka
We present evolutionary models for solar-like stars with an improved treatment of convection that results in a more accurate estimate of the radius and effective temperature. This…
Conditional physics informed neural networks
Alexander Kovacs, Lukas Exl, Alexander Kornell +11
We introduce conditional PINNs (physics informed neural networks) for estimating the solution of classes of eigenvalue problems. The concept of PINNs is expanded to learn not only…
Machine learning methods for the prediction of micromagnetic magnetization dynamics
Sebastian Schaffer, Norbert J. Mauser, Thomas Schrefl +2
Machine learning (ML) entered the field of computational micromagnetics only recently. The main objective of these new approaches is the automatization of solutions of parameter-de…