29 citations · 43 across the 5 of their papers we have counts for
5 papers
Data-Driven Model Reduction and Nonlinear Model Predictive Control of an Air Separation Unit by Applied Koopman Theory
Jan C. Schulze, Danimir T. Doncevic, Nils Erwes +1
Achieving real-time capability is an essential prerequisite for the industrial implementation of nonlinear model predictive control (NMPC). Data-driven model reduction offers a way…
A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms
Danimir T. Doncevic, Alexander Mitsos, Yue Guo +4
Meta-learning of numerical algorithms for a given task consists of the data-driven identification and adaptation of an algorithmic structure and the associated hyperparameters. To…
Hearts Gym: Learning Reinforcement Learning as a Team Event
Jan Ebert, Danimir T. Doncevic, Ramona Kloß +1
Amidst the COVID-19 pandemic, the authors of this paper organized a Reinforcement Learning (RL) course for a graduate school in the field of data science. We describe the strategy…
Identification of MIMO Wiener-type Koopman Models for Data-Driven Model Reduction using Deep Learning
Jan C. Schulze, Danimir T. Doncevic, Alexander Mitsos
We use Koopman theory to develop a data-driven nonlinear model reduction and identification strategy for multiple-input multiple-output (MIMO) input-affine dynamical systems. While…
Personalized Algorithm Generation: A Case Study in Learning ODE Integrators
Yue Guo, Felix Dietrich, Tom Bertalan +4
We study the learning of numerical algorithms for scientific computing, which combines mathematically driven, handcrafted design of general algorithm structure with a data-driven a…