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Near-optimal learning of Banach-valued, high-dimensional functions via deep neural networks

Author(s): Adcock B; Brugiapaglia S; Dexter N; Moraga S;

The past decade has seen increasing interest in applying Deep Learning (DL) to Computational Science and Engineering (CSE). Driven by impressive results in applications such as computer vision, Uncertainty Quantification (UQ), genetics, simulations and image processing, DL is increasingly supplanting classical algorithms, and seems poised to revolutionize ...

Article GUID: 39454372


UncertaintyFuseNet: Robust uncertainty-aware hierarchical feature fusion model with Ensemble Monte Carlo Dropout for COVID-19 detection

Author(s): Abdar M; Salari S; Qahremani S; Lam HK; Karray F; Hussain S; Khosravi A; Acharya UR; Makarenkov V; Nahavandi S;

The COVID-19 (Coronavirus disease 2019) pandemic has become a major global threat to human health and well-being. Thus, the development of computer-aided detection (CAD) systems that are capable of accurately distinguishing COVID-19 from other diseases using chest computed tomography (CT) and X-r ...

Article GUID: 36217534


Development of a DREAM-based inverse model for multi-point source identification in river pollution incidents: Model testing and uncertainty analysis

Author(s): Zhu Y; Chen Z;

Source identification plays a vital role in implementing control measures for sudden river pollution incidents. In contrast to single-point source identification problems, there have been no investigations into inverse identification of multi-point emissions. In this study, an inverse model is developed based on the observed time series of pollutant conce ...

Article GUID: 36191500


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