Despite great progress in simulating multiphysics problems using the numerical
discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate …

Ensemble learning combines several individual models to obtain better generalization
performance. Currently, deep learning architectures are showing better performance …

One of the most important properties of classical neural networks is how surprisingly
trainable they are, though their training algorithms typically rely on optimizing complicated …

As individuals and communities interact in and with an environment that is increasingly
virtual, they are often vulnerable to the commodification of their digital footprint. Concepts …

Along with the great success of deep neural networks, there is also growing concern about
their black-box nature. The interpretability issue affects people's trust on deep learning …

Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

The penetration of renewable energy resources (RES) generation and the interconnection of
regional power grids in wide area and large scale have led the modern power system to …

We propose an end-to-end trainable Convolutional Neural Network (CNN), named
GridDehazeNet, for single image dehazing. The GridDehazeNet consists of three modules …

The purpose of this study is to examine existing deep learning techniques for addressing
class imbalanced data. Effective classification with imbalanced data is an important area of …

We introduce physics-informed neural networks–neural networks that are trained to solve
supervised learning tasks while respecting any given laws of physics described by general …