2 citations · 2 across the 3 of their papers we have counts for
3 papers
Optimizing Variational Quantum Circuits Using Metaheuristic Strategies in Reinforcement Learning
Michael Kölle, Daniel Seidl, Maximilian Zorn +3
Quantum Reinforcement Learning (QRL) offers potential advantages over classical Reinforcement Learning, such as compact state space representation and faster convergence in certain…
Polyconvex neural network models of thermoelasticity
Jan N. Fuhg, Asghar Jadoon, Oliver Weeger +2
Machine-learning function representations such as neural networks have proven to be excellent constructs for constitutive modeling due to their flexibility to represent highly nonl…
Multilevel Monte Carlo estimators for derivative-free optimization under uncertainty
Friedrich Menhorn, Gianluca Geraci, D. Thomas Seidl +3
Optimization is a key tool for scientific and engineering applications, however, in the presence of models affected by uncertainty, the optimization formulation needs to be extende…