The incorporation of physical information in machine learning frameworks is opening and
transforming many application domains. Here the learning process is augmented through …

We develop a method for multifidelity Kolmogorov-Arnold networks (KANs), which use a low-
fidelity model along with a small amount of high-fidelity data to train a model for the high …

The goal of this study is to leverage emerging machine learning (ML) techniques to develop
a framework for the global reconstruction of system variables from potentially scarce and …

Simulating and controlling physical systems described by partial differential equations
(PDEs) are crucial tasks across science and engineering. Recently, diffusion generative …

This study proposes an innovative deep learning-aided approach based on generative
adversarial networks named AttGAN, which is specialized for solving the spatio-temporal …

A super-resolution (SR) method for the reconstruction of Navier-Stokes (NS) flows from noisy
observations is presented. In the SR method, first the observation data is averaged over a …

In science and engineering, machine learning techniques are increasingly successful in
physical systems modeling (predicting future states of physical systems). Effectively …

To mitigate global warming, greenhouse gas sources need to be resolved at a high spatial
resolution and monitored in time to ensure the reduction and ultimately elimination of the …

Abstract Machine learning is increasingly successful in modeling physical systems in
science and engineering. Integrating physical information, like PDEs, can enhance model …