activity
20202022
most citedImplicit energy regularization of neural ordinary-differential-equation control

94 citations · 168 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG2022★ 19 cited

Near-optimal control of dynamical systems with neural ordinary differential equations

Lucas Böttcher, Thomas Asikis

Optimal control problems naturally arise in many scientific applications where one wishes to steer a dynamical system from a certain initial state to a desired targe…

cs.LG2022★ 16 cited

Control of Dual-Sourcing Inventory Systems using Recurrent Neural Networks

Lucas Böttcher, Thomas Asikis, Ioannis Fragkos

A key challenge in inventory management is to identify policies that optimally replenish inventory from multiple suppliers. To solve such optimization problems, inventory managers…

cs.LG2021★ 94 cited

Implicit energy regularization of neural ordinary-differential-equation control

Lucas Böttcher, Nino Antulov-Fantulin, Thomas Asikis

Although optimal control problems of dynamical systems can be formulated within the framework of variational calculus, their solution for complex systems is often analytically and…

cs.LG2020★ 39 cited

Neural Ordinary Differential Equation Control of Dynamics on Graphs

Thomas Asikis, Lucas Böttcher, Nino Antulov-Fantulin

We study the ability of neural networks to calculate feedback control signals that steer trajectories of continuous time non-linear dynamical systems on graphs, which we represent…

cs.CY2020

How Value-Sensitive Design Can Empower Sustainable Consumption

Thomas Asikis, Johannes Klinglmayr, Dirk Helbing +1

In a so-called overpopulated world, sustainable consumption is of existential importance.However, the expanding spectrum of product choices and their production complexity challeng…