Operator learning refers to the application of ideas from machine learning to approximate
(typically nonlinear) operators mapping between Banach spaces of functions. Such …

Transcranial ultrasound imaging has been playing an increasingly important role in the non-
invasive treatment of brain disorders. However, the conventional mesh-based numerical …

Transcranial focused ultrasound (tFUS), in which acoustic energy is focused on a small
region in the brain through the skull, is a non-invasive therapeutic method with high spatial …

In this paper we present a general idea to correct coarse models by introducing a correcting
term designed from fine solutions that can be then applied to situations in which a fine …

Transcranial ultrasound imaging assumes a growing significance in the detection and
monitoring of intracranial lesions and cerebral blood flow. Accurate solution of partial …

We present a deep learning-based iterative approach to solve the discrete heterogeneous
Helmholtz equation for high wavenumbers. Combining classical iterative multigrid solvers …

Iterative methods are ubiquitous in large-scale scientific computing applications, and a
number of approaches based on meta-learning have been recently proposed to accelerate …

Large sparse linear algebraic systems can be found in a variety of scientific and engineering
fields and many scientists strive to solve them in an efficient and robust manner. In this …

In this paper, the authors propose Neumann series neural operator (NSNO) to learn the
solution operator of Helmholtz equation from inhomogeneity coefficients and source terms to …

A volumetric ultrasound tomography (UST) system and method are established for industrial
process applications. A two-plane ring-array UST system is developed for 3-D imaging of the …