206 citations · 234 across the 15 of their papers we have counts for
15 papers
Gain-Scheduling Data-Enabled Predictive Control for Nonlinear Systems with Linearized Operating Regions
Sebastian Zieglmeier, Mathias Hudoba de Badyn, Narada D. Warakagoda +2
This paper presents a Gain-Scheduled Data-Enabled Predictive Control (GS-DeePC) framework for nonlinear systems based on multiple locally linear data representations. Instead of re…
Multi-Horizon Time Series Forecasting of non-parametric CDFs with Deep Lattice Networks
Niklas Erdmann, Lars Bentsen, Roy Stenbro +3
Probabilistic forecasting is not only a way to add more information to a prediction of the future, but it also builds on weaknesses in point prediction. Sudden changes in a time se…
Data-Enabled Predictive Control with Predictive Adaptive Line-of-Sight Guidance for 3-D Path Following of Autonomous Underwater Vehicles
Sebastian Zieglmeier, Mathias Hudoba de Badyn, Narada D. Warakagoda +2
This paper presents a fully data-driven 3-D path-following framework for autonomous underwater vehicles (AUVs), a representative class of underwater field robotics, based on Data-E…
Semi-Data-Driven Model Predictive Control: A Physics-Informed Data-Driven Control Approach
Sebastian Zieglmeier, Mathias Hudoba de Badyn, Narada D. Warakagoda +2
Data-enabled predictive control (DeePC) has emerged as a powerful technique to control complex systems without the need for extensive modeling efforts. However, relying solely on o…
Deep and Probabilistic Solar Irradiance Forecast at the Arctic Circle
Niklas Erdmann, Lars Ø. Bentsen, Roy Stenbro +3
Solar irradiance forecasts can be dynamic and unreliable due to changing weather conditions. Near the Arctic circle, this also translates into a distinct set of further challenges.…
Maximum Manifold Capacity Representations in State Representation Learning
Li Meng, Morten Goodwin, Anis Yazidi +1
The expanding research on manifold-based self-supervised learning (SSL) builds on the manifold hypothesis, which suggests that the inherent complexity of high-dimensional data can…
A Manifold Representation of the Key in Vision Transformers
Li Meng, Morten Goodwin, Anis Yazidi +1
Vision Transformers implement multi-head self-attention via stacking multiple attention blocks. The query, key, and value are often intertwined and generated within those blocks vi…
State Representation Learning Using an Unbalanced Atlas
Li Meng, Morten Goodwin, Anis Yazidi +1
The manifold hypothesis posits that high-dimensional data often lies on a lower-dimensional manifold and that utilizing this manifold as the target space yields more efficient repr…
It is all Connected: A New Graph Formulation for Spatio-Temporal Forecasting
Lars Ødegaard Bentsen, Narada Dilp Warakagoda, Roy Stenbro +1
With an ever-increasing number of sensors in modern society, spatio-temporal time series forecasting has become a de facto tool to make informed decisions about the future. Most sp…
Unsupervised Representation Learning in Partially Observable Atari Games
Li Meng, Morten Goodwin, Anis Yazidi +1
State representation learning aims to capture latent factors of an environment. Contrastive methods have performed better than generative models in previous state representation le…
Spatio-Temporal Wind Speed Forecasting using Graph Networks and Novel Transformer Architectures
Lars Ødegaard Bentsen, Narada Dilp Warakagoda, Roy Stenbro +1
This study focuses on multi-step spatio-temporal wind speed forecasting for the Norwegian continental shelf. The study aims to leverage spatial dependencies through the relative ph…
Deep Reinforcement Learning with Swin Transformers
Li Meng, Morten Goodwin, Anis Yazidi +1
Transformers are neural network models that utilize multiple layers of self-attention heads and have exhibited enormous potential in natural language processing tasks. Meanwhile, t…
Improving the Diversity of Bootstrapped DQN by Replacing Priors With Noise
Li Meng, Morten Goodwin, Anis Yazidi +1
Q-learning is one of the most well-known Reinforcement Learning algorithms. There have been tremendous efforts to develop this algorithm using neural networks. Bootstrapped Deep Q-…
Wind Park Power Prediction: Attention-Based Graph Networks and Deep Learning to Capture Wake Losses
Lars Ødegaard Bentsen, Narada Dilp Warakagoda, Roy Stenbro +1
With the increased penetration of wind energy into the power grid, it has become increasingly important to be able to predict the expected power production for larger wind farms. D…
Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples
Li Meng, Anis Yazidi, Morten Goodwin +1
In this article, we propose a novel algorithm for deep reinforcement learning named Expert Q-learning. Expert Q-learning is inspired by Dueling Q-learning and aims at incorporating…