# Acoustic Structure Inverse Design and Optimization Using Deep Learning

arXiv: https://arxiv.org/abs/2102.02063 · HTML: https://mlsanity.com/paper/2102.02063/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 61 references that are in the map.
- First posted on arXiv 29 Jan 2021.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Deep Learning for Computational Chemistry](https://mlsanity.com/paper/1701.04503/) (2017)
- [Machine learning in acoustics: theory and applications](https://mlsanity.com/paper/1905.04418/) (2019)
- [Deep Learning Microscopy](https://mlsanity.com/paper/1705.04709/) (2017)
- [Deep Learning Reconstruction of Ultra-Short Pulses](https://mlsanity.com/paper/1803.06024/) (2018)
- [Meta-neural-network for Realtime and Passive Deep-learning-based Object Recognition](https://mlsanity.com/paper/1909.07122/) (2019)
- [Probability-Density-Based Deep Learning Paradigm for the Fuzzy Design of Functional Metastructures](https://mlsanity.com/paper/2011.05516/) (2020)

## Built on by (most cited first, 1 of 1)

- [Inverse design of Non-parameterized Ventilated Acoustic Resonator via Variational Autoencoder with Acoustic Response-encoded Latent Space](https://mlsanity.com/paper/2408.05917/) (2024)

## Abstract (arXiv)

From ancient to modern times, acoustic structures have been used to control the propagation of acoustic waves. However, the design of the acoustic structures has remained widely a time-consuming and computational resource-consuming iterative process. In recent years, Deep Learning has attracted unprecedented attention for its ability to tackle hard problems with huge datasets, which has achieved state-of-the-art results in various tasks. In this work, an acoustic structure design method is proposed based on deep learning. Taking the design of multi-order Helmholtz resonator for instance, we experimentally demonstrate the effectiveness of the proposed method. Our method is not only able to give a very accurate prediction of the geometry of the acoustic structures with multiple strong-coupling parameters, but also capable of improving the performance of evolutionary approaches in optimization for a desired property. Compared with the conventional numerical methods, our method is more efficient, universal and automatic, which has a wide range of potential applications, such as speech enhancement, sound absorption and insulation.