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Applied Sciences
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Artificial Intelligence in Acoustic Simulation and Design
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Artificial Intelligence in Acoustic Simulation and Design

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Acoustics and Vibrations".

Deadline for manuscript submissions: closed (20 November 2024) | Viewed by 1165

Special Issue Editors

Dr. Marco Caniato

E-Mail Website
Guest Editor
Faculty of Science and Technology, Free University of Bozen-Bolzano, 39100 Bozen, Italy
Interests: indoor comfort; acoustics; impaired individual comfort perception; acoustic and thermal material characterization; building elements; numerical simulations; sustainability; timber buildings
Special Issues, Collections and Topics in MDPI journals
Dr. Paolo Bonfiglio

E-Mail Website
Guest Editor
Materiacustica s.r.l., 44122 Ferrara, Italy
Interests: porous material vibro-acoustic characterization and design; numerical modeling for the study of acoustical radiation and noise control; active noise control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Acoustics is one of the most highly rated parameters for the built environment and cities. Sound is central to everyday life and it affects the activities and comfort of individuals. It can both cause stress and also be relaxing. This dual effect intrigues acoustics scientists and stimulates new ideas and research every day. At present, humanity is dealing with a new and wide-ranging tool: artificial intelligence. This tool also presents a dual effect: it can be very useful if used correctly or very harmful if misused.

The coupling of artificial intelligence and acoustics is a brand new field of science, and is one that is sure to lead to interesting applications and uses.

For these reasons, this Special Issue, “Artificial Intelligence in Acoustic Simulation and Design”, has been launched. This Special Issue aims to include original research and high-quality review articles on the use of artificial intelligence in every field of acoustics, including its possible applications, the use of datasets, machine learning, simulation-based research, and design. Reports on experimental, computational, or multidisciplinary research are encouraged, and review articles describing the current state of the art are also welcome.

Dr. Marco Caniato
Dr. Paolo Bonfiglio
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • simulation design acoustics

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Published Papers (1 paper)

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Research

23 pages, 1890 KiB  
Article
Physics-Informed Neural Networks for Modal Wave Field Predictions in 3D Room Acoustics
by Stefan Schoder
Appl. Sci. 2025, 15(2), 939; https://doi.org/10.3390/app15020939 - 18 Jan 2025
Viewed by 559
Abstract
The generalization of Physics-Informed Neural Networks (PINNs) used to solve the inhomogeneous Helmholtz equation in a simplified three-dimensional room is investigated. PINNs are appealing since they can efficiently integrate a partial differential equation and experimental data by minimizing a loss function. However, a [...] Read more.
The generalization of Physics-Informed Neural Networks (PINNs) used to solve the inhomogeneous Helmholtz equation in a simplified three-dimensional room is investigated. PINNs are appealing since they can efficiently integrate a partial differential equation and experimental data by minimizing a loss function. However, a previous study experienced limitations in acoustics regarding the source term. A challenging but realistic excitation case is a confined (e.g., single-point) excitation area, yielding a smooth spatial wave field periodically with the wavelength. Compared to studies using smooth (unrealistic) sound excitation, the network’s generalization capabilities regarding a realistic sound excitation are addressed. Different methods like hyperparameter optimization, adaptive refinement, Fourier feature engineering, and locally adaptive activation functions with slope recovery are tested to tailor the PINN’s accuracy to an experimentally validated finite element analysis reference solution computed with openCFS. The hyperparameter study and optimization are conducted regarding the network depth and width, the learning rate, the used activation functions, and the deep learning backends (PyTorch 2.5.1, TensorFlow 2.18.0 1, TensorFlow 2.18.0 2, JAX 0.4.39). A modified (feature-engineered) PINN architecture was designed using input feature engineering to include the dispersion relation of the wave in the neural network. For smoothly (unrealistic) distributed sources, it was shown that the standard PINNs and the feature-engineered PINN converge to the analytic solution, with a relative error of 0.28% and 2×10−4%, respectively. The locally adaptive activation functions with the slope lead to a relative error of 0.086% with a source sharpness of s=1 m. Similar relative errors were obtained for the case s=0.2 m using adaptive refinement. The feature-engineered PINN significantly outperformed the results of previous studies regarding accuracy. Furthermore, the trainable parameters were reduced to a fraction by Bayesian hyperparameter optimization (around 5%), and likewise, the training time (around 3%) was reduced compared to the standard PINN formulation. By narrowing this excitation towards a single point, the convergence rate and minimum errors obtained of all presented network architectures increased. The feature-engineered architecture yielded a one order of magnitude lower accuracy of 0.20% compared to 0.019% of the standard PINN formulation with a source sharpness of s=1 m. It outperformed the finite element analysis and the standard PINN in terms time needed to obtain the solution, needing 15 min and 30 s on an AMD Ryzen 7 Pro 8840HS CPU (AMD, Santa Clara, CA, USA) for the FEM, compared to about 20 min (standard PINN) and just under a minute of the feature-engineered PINN, both trained on a Tesla T4 GPU (NVIDIA, Santa Clara, CA, USA). Full article
(This article belongs to the Special Issue Artificial Intelligence in Acoustic Simulation and Design)
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