Federated learning (FL) is a privacy-preserving collaboratively machine learning paradigm. Traditional FL requires all data owners (a.k.a. FL clients) to train the same local model. This design is not well-suited for scenarios involving data and/or system ...
While metasurfaces (MSs) are constructed from deeply subwavelength unit cells, they are generally electrically large and full-wave simulations of the complete structure are computationally expensive. Thus, to reduce this high computational cost, nonuniform ...
This paper presents an experimental validation of a new design method for adaptive structures that counteract the effect of loading through shape morphing. The structure is designed to morph into target shapes that are optimal to take external loads throug ...
Active Debris Removal missions consist of sending a satellite in space and removing one or more debris from their current orbit. A key challenge is to obtain information about the uncooperative target. By gathering the velocity, position, and rotation of t ...
Soft Magnetic Composites (SMCs) possess promising electromagnetic characteristics and attract intense research and application interest in the engineering community. Fabrication of composites with customized architecture is feasible due to the recent advan ...
Institute of Electrical and Electronics Engineers2021
We present a novel algorithm based on the ensemble Kalman filter to solve inverse problems involving multiscale elliptic partial differential equations. Our method is based on numerical homogenization and finite element discretization and allows us to reco ...
