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
Data Publishing in Mechanics and Dynamics: Challenges, Guidelines, and Examples from Engineering Design
Henrik Ebel, Jan van Delden, Timo Lüddecke, Aditya Borse, Rutwik Gulakala, Marcus Stoffel, Manish Yadav, Merten Stender, Leon Schindler, Kristin Miriam de Payrebrune, Maximilian Raff, C. David Remy, Benedict Röder, Rohit Raj, Tobias Rentschler, Alexander Tismer, Stefan Riedelbauch, Peter Eberhard
Predictive Spliner: Data-Driven Overtaking in Autonomous Racing Using Opponent Trajectory Prediction
Nicolas Baumann, Edoardo Ghignone, Cheng Hu, Benedict Hildisch, Tino Hämmerle, Alessandro Bettoni, Andrea Carron, Lei Xie, Michele Magno