62 citations · 155 across the 41 of their papers we have counts for
6 papers · 1 filter
Control Strategies for Transport Networks under Demand Uncertainty
Simone Göttlich, Thomas Schillinger
In this article, we consider transport networks with uncertain demands. Network dynamics are given by linear hyperbolic partial differential equations and suitable coupling conditi…
Parameter calibration with Consensus-based Optimization for interaction dynamics driven by neural networks
Simone Göttlich, Claudia Totzeck
We calibrate parameters of neural networks that model forces in interaction dynamics with the help of the Consensus-based global optimization method (CBO). We state the general fra…
Efficient simulation of coupled gas and power networks under uncertain demands
E. Fokken, S. Göttlich, M. Herty
We introduce an approach and a software tool, Grazer, for solving coupled energy networks composed of gas and electric power networks. Those networks are coupled to stochastic fluc…
Network models for nonlocal traffic flow
Jan Friedrich, Simone Göttlich, Maximilian Osztfalk
We present a network formulation for a traffic flow model with nonlocal velocity in the flux function. The modeling framework includes suitable coupling conditions at intersections…
Space mapping-based optimization with the macroscopic limit of interacting particle systems
Jennifer Weißen, Simone Göttlich, Claudia Totzeck
We propose a space mapping-based optimization algorithm for microscopic interacting particle dynamics which are inappropriate for direct optimization. This is of relevance for exam…
Optimal control for interacting particle systems driven by neural networks
Simone Göttlich, Claudia Totzeck
We propose a neural network approach to model general interaction dynamics and an adjoint based stochastic gradient descent algorithm to calibrate its parameters. The parameter cal…