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08/23/2023
A multiobjective continuation method to compute the regularization path of deep neural networks
Sparsity is a highly desired feature in deep neural networks (DNNs) sinc...
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07/28/2023
Partial observations, coarse graining and equivariance in Koopman operator theory for large-scale dynamical systems
The Koopman operator has become an essential tool for data-driven analys...
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02/14/2023
Learning a model is paramount for sample efficiency in reinforcement learning control of PDEs
The goal of this paper is to make a strong point for the usage of dynami...
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01/25/2023
Distributed Control of Partial Differential Equations Using Convolutional Reinforcement Learning
We present a convolutional framework which significantly reduces the com...
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09/20/2022
Learning Bilinear Models of Actuated Koopman Generators from Partially-Observed Trajectories
Data-driven models for nonlinear dynamical systems based on approximatin...
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04/08/2021
Efficient time stepping for numerical integration using reinforcement learning
Many problems in science and engineering require the efficient numerical...
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02/26/2021
Derivative-Free Multiobjective Trust Region Descent Method Using Radial Basis Function Surrogate Models
We present a flexible trust region descend algorithm for unconstrained a...
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02/09/2021
On the Universal Transformation of Data-Driven Models to Control Systems
As in almost every other branch of science, the major advances in data s...
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12/14/2020
On the Treatment of Optimization Problems with L1 Penalty Terms via Multiobjective Continuation
We present a novel algorithm that allows us to gain detailed insight int...
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09/23/2019
Data-driven approximation of the Koopman generator: Model reduction, system identification, and control
We derive a data-driven method for the approximation of the Koopman gene...
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05/24/2019