Deep neural networks trained on physical losses are emerging as promising surrogates for
nonlinear numerical solvers. These tools can predict solutions to Maxwell's equations and …

Self-referential holographic data storage (SR-HDS), which has been proposed as a novel
implementation method for holographic data storage (HDS), enables holographic recording …

Recently, it was shown that a detailed reconstruction of urban height maps is possible from
single very high resolution (VHR) synthetic aperture radar (SAR) images with deep …

The deep learning-based estimation of topographic heights from single remote sensing
images has shown great potential in recent years. Drawing inspiration from the computer …

We propose an alternative data-free deep learning method using a physics-informed neural
network (PINN) to enable more efficient computation of light diffraction from 3D optical …

Generating high-fidelity landscape paintings presents significant challenges, necessitating
precise control over both structure and style. This paper introduces LPGen, a novel diffusion …

A deep learning based method is proposed to predict the urban air mobility (UAM) noise
propagation in the urban environment. This method aims to efficiently estimate the noise …

Multimodal imaging and correlative analysis typically require image alignment. Contrastive
learning can generate representations of multimodal images, reducing the challenging task …