arXiv:2607. 18164v1 Announce Type: cross Abstract: Digital Twins rely on surrogate models to mirror physical systems in real time, yet these models can degrade as operating conditions evolve, a phenomenon known as concept drift.
By Yi-Ping Chen, Ying-Kuan Tsai, Vispi Karkaria, Seul Lee, Daniel Apley, Wei Chen
arXiv:2606. 15053v1 Announce Type: new Abstract: Surrogate models are central to scientific machine learning, where they enable fast prediction, simulation, inference, and control for complex physical systems.
By Matthias Chung, Yutong Bu, Deepanshu Verma
arXiv:2606. 16219v1 Announce Type: cross Abstract: Digital twin modeling, including control and data assimilation under model uncertainty, often faces an open-ended fidelity problem: adding variables, data streams, and time scales can indefinitely increase model complexity, ultimately producing systems that are difficult to maintain, validate, interpret, and use for stress or safety testing.
By Zongren Zou, Th\'eo Bourdais, Ricardo Baptista, Houman Owhadi
arXiv:2603. 22050v2 Announce Type: replace-cross Abstract: Supervised machine learning describes the practice of fitting a parameterized model to labeled input-output data.
By Atticus Rex, Elizabeth Qian, David Peterson
arXiv:2607. 15077v1 Announce Type: new Abstract: Many engineering problems involve phenomena whose governing equations are poorly characterized or only partially known.
By Yao Cheng Li, Ana Larra\~naga, Steven L. Brunton, Urban Fasel
arXiv:2505. 18201v2 Announce Type: replace-cross Abstract: Controlling flapping-wing drones requires controllers that handle time-varying, nonlinear, underactuated dynamics from incomplete, noisy sensor data.
By Romain Poletti, Lorenzo Schena, Lilla Koloszar, Joris Degroote, Miguel Alfonso Mendez