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Physics-informed machine learning for micro-perforated panels: reproducible prediction, uncertainty, and design optimization

*Corresponding author for this work
Research Output:
Contribution to journal
Article
 Peer-review

Abstract

Porosity, hole radius, panel thickness, and thickness-to-diameter ratio govern sound absorption in micro-perforated panels (MPPs) through viscous–thermal losses in submillimeter apertures. A physics-informed machine-learning workflow is presented for predicting the frequency-averaged absorption coefficient "alpha" combining acoustically motivated feature design with probabilistic prediction and calibrated uncertainty. A quality-controlled dataset of 1000 MPP geometries spanning hole radius, porosity, thickness, perforation-shape class, and frequency-averaged absorption coefficient was used to benchmark 28 regression pipelines under leakage-safe fivefold cross-validation. A smooth Maa-inspired prior, monotone in porosity and saturating in thickness ratio, was combined with residual learning. Gaussian-process (GP) variants were the strongest single learners, with the Matern-kernel GP and physics-informed (PI) residual GP both achieving root mean square error = 0.138 and R2 = 0.763. Physics-informed Gaussian-process (PI-GP) posteriors provided near-nominal 95% coverage (0.945; mean width = 0.485), whereas split-conformal intervals were more conservative (coverage = 0.970; mean width = 0.573). Measured impedance-tube spectra for six additively manufactured circular MPPs using the Cavity 1 configuration were added as independent validation data. All measured Cavity 1 absorption values fell within the GP and conformal 95% intervals, supporting uncertainty consistency while revealing point-prediction discrepancies associated with unmodeled cavity, fabrication, mounting, and layout effects.

Publication Information

Output type

Research Output:
Contribution to journal
Article
 Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 1001-1010 (10 pages)

Journal (Volume, Issue Number)

Journal of the Acoustical Society of America (Volume 160, Issue 2)

Publication milestones

  • Accepted/In press - 13/07/2026
  • Published - 03/08/2026

Publication status

Published - 03/08/2026

ISSN

0001-4966

Publication IDs

  • Scopus: 105046381546
  • PubMed: 42545051

Access to documents

Bainamndi - Physics-informed MPP
Final published version, 1.43 MB
Access to file: Embargo ends 03/08/2027

Link

Handle.net

Persistent link