arXiv:2608. 16873v1 Announce Type: new Abstract: High-fidelity finite-element simulations can provide accurate numerical predictions for side-branch resonators, but large simulation datasets are expensive to generate and purely data-driven surrogates may become unreliable when simulation-labelled data are scarce.
By Jiaming Li
The paper presents a data‑efficient machine learning framework that incorporates a low‑cost analytical model to enhance predictions of sound‑reduction frequencies in Helmholtz resonators. Two strategies are explored: (1) using the analytical model as a baseline and learning only the discrepancy with limited high‑fidelity simulation data, and (2) distilling the analytical mapping into a learned prior and calibrating it with scarce simulation data. Experiments on rectangular side‑branch resonators show that both approaches significantly reduce prediction error compared to direct learning, achieving mean absolute errors as low as 0.371 Hz with full‑model fine‑tuning.
By Jiaming Li
arXiv:2607. 04407v1 Announce Type: cross Abstract: Repeated prediction of acoustic fields from spatially distributed boundary excitation is computationally expensive when each source realization requires a new wave simulation.
By Muhammad Idrees Khan, Hua-Dong Yao
arXiv:2607. 14193v1 Announce Type: cross Abstract: The Helmholtz equation governs time-harmonic wave propagation, and in dissipative media a complex modulus renders its squared wavenumber $\kappa^2$ complex.
By Boyuan Deng, Kshitiz Upadhyay, Michael Shields
arXiv:2603. 22050v2 Announce Type: replace-cross Abstract: Supervised machine learning describes the practice of fitting a parameterized model to labeled input-output data.
By Atticus Rex, Elizabeth Qian, David Peterson
arXiv:2608. 16152v1 Announce Type: cross Abstract: We study resonance prediction in dispersive media, formulated as nonlinear spectral problems for volume integral operators.
By Konstantinos Alexopoulos, Josselin Garnier