We combine vision transformers with operator learning to solve diverse inverse problems
described by partial differential equations (PDEs). Our approach, named Vision Transformer …

Time-To-First-Spike (TTFS) coding in Spiking Neural Networks (SNNs) offers significant
advantages in terms of energy efficiency, closely mimicking the behavior of biological …

Experimental blood flow measurement techniques are invaluable for a better understanding
of cardiovascular disease formation, progression, and treatment. One of the emerging …

A deep neural network is developed as a symmetry-informed surrogate model to estimate
the stress of acoustic waves at any arbitrary location, time, and wave excitation width, as a …

We consider an inverse elastic scattering problem of simultaneously reconstructing a rigid
obstacle and the excitation sources using near-field measurements. Specifically, we are …

A six degree-of-freedom (DOF) motion control system for docking with a deep submergence
rescue vehicle (DSRV) test platform was the focus of this study. The existing control methods …

The paper falls into the category of computational methods for inverse scattering techniques
for the identification of scatterers. We consider a linear elastodynamic problem and compare …

Modeling piezoelectric elements (piezos) using the finite element method with electro-
mechanical coupling requires significant computational resources. The electro-mechanical …

In the information age, data-driven methods are an increasingly popular tool used in
science, engineering, business, and many other fields of study. Some of these methods …