Numerical simulation of fluids plays an essential role in modeling many physical
phenomena, such as weather, climate, aerodynamics, and plasma physics. Fluids are well …

Viewing recurrent neural networks (RNNs) as continuous-time dynamical systems, we
propose a recurrent unit that describes the hidden state's evolution with two parts: a well …

We propose heavy ball neural ordinary differential equations (HBNODEs), leveraging the
continuous limit of the classical momentum accelerated gradient descent, to improve neural …

Fast and accurate MRI image reconstruction from undersampled data is crucial in clinical
practice. Deep learning based reconstruction methods have shown promising advances in …

This paper studies the stability and convergence properties of a class of multi-agent
concurrent learning (CL) algorithms with momentum and restart. Such algorithms can be …

Implicit graph neural networks (IGNNs)–that solve a fixed-point equilibrium equation using
Picard iteration for representation learning–have shown remarkable performance in learning …

We propose the Nesterov neural ordinary differential equations (NesterovNODEs), whose
layers solve the second-order ordinary differential equations (ODEs) limit of Nesterov's …

Abstract Stochastic Gradient Descent (SGD) series optimization methods play the vital role
in training neural networks, attracting growing attention in science and engineering fields of …

Rocks under stress deform by creep mechanisms that include formation and slip on small‐
scale internal cracks. Intragranular cracks and slip along grain contacts release energy as …

Abstract Handwritten Mathematical Expression Recognition (HMER) is essential to online
education and scientific research. However, discerning the length and characters of …