Dynamic data-driven model reduction: adapting reduced models from incomplete data

Dynamic data-driven model reduction: adapting reduced models from incomplete data

Correspondence: [email protected] Department of Aeronautics & Astronautics, MIT, 77 Massachusetts Avenue, 02139 Cambridge, USA Full list of author information is available at the end of the article Abstract This work presents a data-driven online adaptive model reduction approach for systems that undergo dynamic changes. Classical model reduction constructs a reduced model of a large-scale syste...

Image Reconstruction from Incomplete Data

Image Reconstruction from Incomplete Data

Detecting and Adapting to Parameter Changes for Reduced Models of Dynamic Data-driven Application Systems

We consider the task of dynamic capability estimation for an unmanned aerial vehicle, which is needed to provide the vehicle with the ability to dynamically and autonomously sense, plan, and act in real time. Our dynamic data-driven application systems framework employs reduced models to achieve rapid evaluation runtimes. Our reduced models must also adapt to underlying dynamic system changes, ...

Data-Driven Reduced Model Construction with Time-Domain Loewner Models

This work presents a data-driven nonintrusive model reduction approach for largescale time-dependent systems with linear state dependence. Traditionally, model reduction is performed in an intrusive projection-based framework, where the operators of the full model are required either explicitly in an assembled form or implicitly through a routine that returns the action of the operators on a ve...