Operator learning is a rising field of scientific computing where inputs or outputs of a machine learning model are functions defined in infinite-dimensional spaces. In this paper, we introduce Neon (Neural Epistemic Operator Networks), an architecture for generating predictions with uncertainty using a single operator network backbone, which presents orders of magnitude less trainable parameters than deep ensembles of comparable performance. We showcase the utility of this method for sequential decision-making by examining the problem of composite Bayesian Optimization (BO), where we aim to optimize a function , where is an unknown map which outputs elements of a function space, and is a known and cheap-to-compute functional.
View Article and Find Full Text PDFBackground: With its abrupt and huge health and socio-economic consequences, the coronavirus disease (COVID-19) pandemic has led to a uniquely demanding, intensely stressful, and even traumatic period. Healthcare workers (HCW), especially nurses, were exposed to mental health challenges during those challenging times.
Objectives: Review the current literature on mental health problems among nurses caring for COVID-19 patients.
Recently, deep learning surrogates and neural operators have shown promise in solving partial differential equations (PDEs). However, they often require a large amount of training data and are limited to bounded domains. In this work, we present a novel physics-informed neural operator method to solve parameterized boundary value problems without labeled data.
View Article and Find Full Text PDFDuring the COVID-19 pandemic, numerous studies have shown the high prevalence of occupational stress (OS) of health workers, affecting the quality of health care provided. To date, there is no study regarding OS of emergency care pediatric nurses working in Greece. This study aimed to examine the pediatric nurses' OS working in tertiary public hospitals in Greece.
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