Hybrid algorithms, which combine black-box machine learning methods with experience
from traditional numerical methods and domain expertise from diverse application areas, are …

We propose to enhance the training of physics-informed neural networks. To this aim, we
introduce nonlinear additive and multiplicative preconditioning strategies for the widely used …

Deep convolutional neural networks (CNNs) have been shown to be very successful in a
wide range of image processing applications. However, due to their increasing number of …

In many modern computer application problems, the classification of image data plays an
important role. Among many different supervised machine learning models, convolutional …

The segmentation of ultra-high resolution images poses challenges such as loss of spatial
information or computational inefficiency. In this work, a novel approach that combines …

We propose to train neural networks (NNs) using a novel variant of the``Additively
Preconditioned Trust-region Strategy''(APTS). The proposed method is based on a …

With the rapid growth of data centers, optimizing energy consumption has become a critical
challenge. This paper proposes an energy management framework that integrates Long …

This work exclusively focuses on using convolution neural operator learning for accelerating
the solution of some heterogenous PDEs (including Poisson equations, Darcy flow …

In power monitoring and management, accurate acquisition of meter reading is critical for
energy analysis and electricity calculation. However, most of the existing electricity meters …