The general method of graph coarsening or graph reduction has been a remarkably useful
and ubiquitous tool in scientific computing and it is now just starting to have a similar impact …

A highly sensitive mid-infrared dual-range real-time trace sensor was developed for ethane
detection in exhaled breath, in which a continuous-wave (CW) mode interband cascade …

Diffusion models have recently emerged as powerful generative priors for solving inverse
problems. However, training diffusion models in the pixel space are both data intensive and …

We propose using operator learning to approximate the dynamical response of non-autonomous
systems, such as nonlinear control systems. Unlike classical function learning, operator …

In the context of sustainable urban development, effective garbage management plays a
crucial role. However, traditional methods encounter limitations in terms of data quality and …

Physics-informed neural network (PINN) is a data-driven approach to solving equations. It is
successful in many applications; however, the accuracy of the PINN is not satisfactory when …

The Deep Operator Network (DeepONet) is a neural network architecture used to approximate
operators, including the solution operator of parametric PDEs. DeepONets have shown …

Stepping from sentence-level to document-level, the research on relation extraction (RE)
confronts increasing text length and more complicated entity interactions. Consequently, it is …

The development of autonomous agents increasingly relies on Multimodal Language Models
(MLMs) to perform tasks described in natural language with GUI environments, such as …

Approximating nonlinear differential equations using a neural network provides a robust and
efficient tool for various scientific computing tasks, including real-time predictions, inverse …