4 papers
Surface impedance inference via neural fields and sparse acoustic data obtained by a compact array
Yuanxin Xia, Xinyan Li, Matteo Calafà +2
Standardized laboratory characterizations for absorbing materials rely on idealized sound field assumptions, which deviate largely from real-life conditions. Consequently, \emph{in…
HergNet: a Fast Neural Surrogate Model for Sound Field Predictions via Superposition of Plane Waves
Matteo CalafÃ, Yuanxin Xia, Cheol-Ho Jeong
We present a novel neural network architecture for the efficient prediction of sound fields in two and three dimensions. The network is designed to automatically satisfy the Helmho…
A holomorphic Kolmogorov-Arnold network framework for solving elliptic problems on arbitrary 2D domains
Matteo CalafÃ, Tito Andriollo, Allan P. Engsig-Karup +1
Physics-informed holomorphic neural networks (PIHNNs) have recently emerged as efficient surrogate models for solving differential problems. By embedding the underlying problem str…
A stable decoupled perfectly matched layer for the 3D wave equation using the nodal discontinuous Galerkin method
Sophia Julia Feriani, Matthias Cosnefroy, Allan Peter Engsig-Karup +3
In outdoor acoustics, the calculations of sound propagating in air can be computationally heavy if the domain is chosen large enough to fulfil the Sommerfeld radiation condition. B…