117 citations
- University of KaiserslauternDE103 papers
- University of Koblenz and LandauDE28 papers
- German Research Centre for Artificial IntelligenceDE25 papers
- Fraunhofer Institute for Industrial MathematicsDE13 papers
- Centre National de la Recherche ScientifiqueFR9 papers
- Johannes Gutenberg University MainzDE8 papers
- Ludwig-Maximilians-Universität MünchenDE7 papers
- RWTH Aachen UniversityDE7 papers
- Daimler (Germany)DE6 papers
- Universität HamburgDE6 papers
- Forschungszentrum JülichDE5 papers
- Sorbonne UniversitéFR5 papers
160 papers
Generalized s-d model for Wannier-Mott excitons in layered magnetic semiconductors
Sonu Verma, Bashab Dey, Akashdeep Kamra
The recent discovery of excitons coupled to the magnetic order, and the consequent strong magneto-optic responses, in some van der Waals magnetic semiconductors has triggered inten…
Epitaxial with intrinsic magnetocrystalline anisotropy as a route to bias-field-free nonlinear half-metal magnonics at the nanoscale
Anna Maria Friedel, Jaafar Ghanbaja, Björn Heinz +5
Half-metallic Heusler compounds like allow to bridge magnonic and spintronic functionality for hybrid unconventional computing approaches with sought-after prop…
Exploring and Exploiting Synchrony Limitations of Time-Triggered Network-Agnostic Guardians
Shreya Vithal Kulhalli, Mohammad Ibrahim Alkoudsi, Gerhard Fohler
Time-triggered communication protocols rely on trusted components known as guardians to enforce adherence to predetermined network schedules. Network-agnostic guardians offer an ef…
The quantum harmonic oscillator in a dissipative bath of anyon pairs
Nils-Henrik Meyer, Axel Pelster, Michael Thorwart
We generalize the formalism of open quantum systems to introduce anyon baths. In particular, we work out a dissipative anyon bath composed of independent pairs of one-dimensional G…
Automated Batch Distillation Process Simulation for a Large Hybrid Dataset for Deep Anomaly Detection
Jennifer Werner, Justus Arweiler, Indra Jungjohann +4
Anomaly detection (AD) in chemical processes based on deep learning offers significant opportunities but requires large, diverse, and well-annotated training datasets that are rare…
Discrete adjoint gradient computation for multiclass traffic flow models on road networks
Paola Goatin, Axel Klar, Carmen Mezquita-Nieto
This paper applies a discrete adjoint gradient computation method for a multi-class traffic flow model on road networks. Vehicle classes are characterized by their specific velocit…