Lessons Learned from Data-Driven Building Control Experiments: Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement Learning

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Buildings play a pivotal role in the ongoing worldwide energy transition, accounting for 30% of the global energy consumption. With traditional engineering solutions reaching their limits to tackle such large-scale problems, data-driven methods and Machine ...

Much attention has been paid to dynamical simulation and quantum machine learning (QML) independently as applications for quantum advantage, while the possibility of using QML to enhance dynamical simulations has not been thoroughly investigated. Here we d ...

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This dataset contains a collection of ultrafast ultrasound acquisitions from nine volunteers and the CIRS 054G phantom. For a comprehensive understanding of the dataset, please refer to the paper: Viñals, R.; Thiran, J.-P. A KL Divergence-Based Loss for In ...

Lessons Learned from Data-Driven Building Control Experiments: Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement Learning

Chattez avec Graph Search

Fusing Pre-existing Knowledge and Machine Learning for Enhanced Building Thermal Modeling and Control

Dynamical simulation via quantum machine learning with provable generalization

Inverse design of metal-organic frameworks for direct air capture of CO2via deep reinforcement learning

Modified capillary number to standardize droplet generation in suction-driven microfluidics

RE:SLAB — a load bearing system for open-ended component reuse in building structures

Strategy to systematically design and deploy the ITER plasma control system: A system engineering and model-based design approach

A semantic model-based systems engineering approach for assessing the operational performance of metal forming process

Oligolithic Cross-task Optimizations across Isolated Workloads

Predicting the long-term collective behaviour of fish pairs with deep learning

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

An aircraft assembly process formalism and verification method based on semantic modeling and MBSE

Glenohumeral joint force prediction with deep learning

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Incorporating Projective Geometry into Deep Learning

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life

Understanding generalization and robustness in modern deep learning

Safe Deep Neural Networks

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (1/6)