5 papers
Physics-informed neural networks for solving saddle-point equations in strong-field physics with tailored fields
Jiakang Chen, Sufia Hashim, Carla Figueira de Morisson Faria
We develop an unsupervised physics-informed neural network to solve saddle-point equations (SPEs) governing direct above-threshold ionization (ATI) within the strong-field approxim…
CycleULM: A unified label-free deep learning framework for ultrasound localisation microscopy
Su Yan, Clara Rodrigo Gonzalez, Vincent C. H. Leung +11
Super-resolution ultrasound via microbubble (MB) localisation and tracking, also known as ultrasound localisation microscopy (ULM), can resolve microvasculature beyond the acoustic…
Neural PDE Solvers with Physics Constraints: A Comparative Study of PINNs, DRM, and WANs
Jiakang Chen
Partial differential equations (PDEs) underpin models across science and engineering, yet analytical solutions are atypical and classical mesh-based solvers can be costly in high d…
SING: Semantic Image Communications using Null-Space and INN-Guided Diffusion Models
Jiakang Chen, Selim F. Yilmaz, Di You +2
Joint source-channel coding systems based on deep neural networks (DeepJSCC) have recently demonstrated remarkable performance in wireless image transmission. Existing methods prim…
CommIN: Semantic Image Communications as an Inverse Problem with INN-Guided Diffusion Models
Jiakang Chen, Di You, Deniz Gündüz +1
Joint source-channel coding schemes based on deep neural networks (DeepJSCC) have recently achieved remarkable performance for wireless image transmission. However, these methods u…