A Data-Efficient Analytical Prior Machine Learning Framework for Sound Reduction Frequency Prediction in Helmholtz Resonators
Read the original on arXiv Machine Learning →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.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.