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

Quantum computers have demonstrable ability to solve problems at a scale beyond brute-
force classical simulation. Interest in quantum algorithms has developed in many areas …

The remarkable practical success of deep learning has revealed some major surprises from
a theoretical perspective. In particular, simple gradient methods easily find near-optimal …

Advances in computing technology have fostered the development of new and powerful
deep learning (DL) techniques, which have demonstrated promising results in a wide range …

Deep learning has achieved remarkable success in diverse applications; however, its use in
solving partial differential equations (PDEs) has emerged only recently. Here, we present an …

The prospect of achieving quantum advantage with quantum neural networks (QNNs) is
exciting. Understanding how QNN properties (for example, the number of parameters M) …

Deep learning-based techniques have achieved state-of-the-art performance on a wide
variety of recognition and classification tasks. However, these networks are typically …

Deep neural networks (DNNs) have demonstrated dominating performance in many fields;
since AlexNet, networks used in practice are going wider and deeper. On the theoretical …

Artificial intelligence techniques are excessively used in computing for training, forecasting
and evaluation purposes. Among these techniques, artificial neural network (ANN) is widely …

Physics-informed neural networks (PINNs), introduced in M. Raissi, P. Perdikaris, and G.
Karniadakis, J. Comput. Phys., 378 (2019), pp. 686--707, are effective in solving integer …