Machine learning aided near-field acoustic holography based on equivalent source method

Chaitanya, S. K.; Sriraman, Siddharth; Srinivasan, Srinath; Srinivasan, K.

doi:10.1121/10.0017115

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…These simulations were derived from COMSOL Multiphysics. While acknowledging that these datasets originate from simulations, it is crucial to highlight that employing simulated data for machine learning in diverse fields, including acoustic studies, has been validated in prior research endeavors [39,48]. In order to verify the computational study, the band diagram of the COMSOL result is compared with the PWE method [19,23].…”

Section: Validationmentioning

confidence: 99%

Data-driven approach for prediction of band diagram of locally resonant sonic crystal

Karthik,

Barathula,

Srinivasan

2024

J. Phys. D: Appl. Phys.

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The present study focuses on the prediction of the band diagram of Locally Resonance Sonic Crystal (LRSC) using machine learning models from structural, lattice material parameters. The data set for the study is generated from the numerical simulations of LRSC for wide ranges of structural, lattice material parameters using COMSOL multiphysics solver. Three machine learning algorithms namely, Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest Regression (RFR), are employed for the study. A total of 23040 samples are simulated and included in the training of the models. Hyperparameter tuning and data set size optimization are carried out to find the optimum machine learning model given the minimum required data set. The R2 score, Root Mean Square Error (RMSE), computational time, and inference time are used to evaluate the performance of machine learning algorithms. The feature importance scores and partial dependence plots (PDP) are used to understand each input feature's critical role in predicting the band diagram. Among all the machine learning models studied, RFR outperformed the ANN and SVM models. RFR with 50 decision trees performed well. However, RFR with 10 decision trees also performed well with slight higher RMSE offering lower inference time compared to RFR with 50 decision trees. Due to the importance of the first two band gaps in noise attenuation, the width and center frequency of the first two bandgaps are predicted through the band diagram. The feature importance scores and Partial Dependence Plots of RFR models show that the structural and lattice parameters play a crucial role in predicting the first and second bandgap, aligning with physical significance.

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Section: Validationmentioning

confidence: 99%

Data-driven approach for prediction of band diagram of locally resonant sonic crystal

Karthik,

Barathula,

Srinivasan

2024

J. Phys. D: Appl. Phys.

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Surface velocity reconstruction of a vibrating structure based on dictionary learning and sparse sampling

Liu,

et al. 2024

Wave Motion

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Sound field recovery based on numerical data-driven and equivalent source method

Liu,

Hu,

2023

Journal of Vibration and Control

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Sparse representation has been applied to realize an accurate description of the sound field from limited set of measurements. The prerequisite of this application is that sound field can be sparsely represented in a specific basis. However, most of the sparse bases are constructed by using the physical models of sound field, and are only effective for a specific category of sound sources. In this paper, the data-driven dictionary learning approach is exploited to obtain a sparse basis of sound field. Meanwhile, to reduce the difficulty and workload of the collection of data sample, the equivalent source method is utilized to collect data samples by means of simulations and thus the data samples can be generated by taking advantage of sound field properties. The performance of the sound field recovery with sparse sampling based on learned dictionary is examined and is compared with those based on other sparse bases. The result indicates that numerical data-driven model is more flexible and is not limited to a specific category of sound sources. The advantage of the proposal is presented by the results of numerical simulation and experiment.

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Machine learning aided near-field acoustic holography based on equivalent source method

Cited by 3 publications

References 17 publications

Data-driven approach for prediction of band diagram of locally resonant sonic crystal

Data-driven approach for prediction of band diagram of locally resonant sonic crystal

Surface velocity reconstruction of a vibrating structure based on dictionary learning and sparse sampling

Sound field recovery based on numerical data-driven and equivalent source method

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