Detectors trained with massive labeled data often exhibit dramatic performance degradation
in some particular scenarios with data distribution gap. To alleviate this problem of domain …

Neural operators generalize classical neural networks to maps between infinite-dimensional
spaces, eg, function spaces. Prior works on neural operators proposed a series of novel …

The practical application of structural health monitoring is often limited by the availability of
labelled data. Transfer learning–specifically in the form of domain adaptation (DA)–gives …

Generalizing knowledge to unseen domains, where data and labels are unavailable, is
crucial for machine learning models. We tackle the domain generalization problem to learn …

We use information-theoretic tools to derive a novel analysis of Multi-source Domain
Adaptation (MDA) from the representation learning perspective. Concretely, we study joint …

Applying data-driven methods such as deep learning in material mechanics is challenging
because producing a sufficiently large, labeled dataset is costly resource-wise. This paper …

A widely recognized difficulty in federated learning arises from the statistical heterogeneity
among clients: local datasets often originate from distinct yet not entirely unrelated …

A widely recognized difficulty in federated learning arises from the statistical heterogeneity
among clients: local datasets often come from different but not entirely unrelated …

Physical systems are extending their monitoring capacities to edge areas with low-cost, low-
power sensors and advanced data mining and machine learning techniques. However, new …

In recent years, most existing domain-adapted bearing fault diagnoses for rotating
machinery have been designed to decrease domain drifts for various operating conditions …