Data Driven
Data-driven approaches are revolutionizing scientific research and engineering by leveraging vast datasets to build predictive models and automate complex tasks. Current research focuses on developing and refining algorithms like neural networks (including transformers and graph neural networks), Gaussian processes, and ADMM for diverse applications, ranging from autonomous systems and financial forecasting to scientific discovery and healthcare. This shift towards data-centric methodologies promises to accelerate scientific progress and improve the efficiency and effectiveness of various technological systems, particularly in areas where traditional modeling approaches are limited by complexity or data scarcity.
Papers
Learning battery model parameter dynamics from data with recursive Gaussian process regression
Antti Aitio, Dominik Jöst, Dirk Uwe Sauer, David A. Howey
Development of a Realistic Crowd Simulation Environment for Fine-grained Validation of People Tracking Methods
Paweł Foszner, Agnieszka Szczęsna, Luca Ciampi, Nicola Messina, Adam Cygan, Bartosz Bizoń, Michał Cogiel, Dominik Golba, Elżbieta Macioszek, Michał Staniszewski
A Data-Driven Model-Reference Adaptive Control Approach Based on Reinforcement Learning
Mohammed Abouheaf, Wail Gueaieb, Davide Spinello, Salah Al-Sharhan
Towards Real-World Applications of Personalized Anesthesia Using Policy Constraint Q Learning for Propofol Infusion Control
Xiuding Cai, Jiao Chen, Yaoyao Zhu, Beimin Wang, Yu Yao