Generative adversarial networks for the design of acoustic metamaterials

Gurbuz, Caglar; Kronowetter, Felix; Dietz, Christoph; Eser, Martin; Schmid, Jonas; Marburg, Steffen

doi:10.1121/10.0003501

Cited by 79 publications

(34 citation statements)

References 33 publications

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“…Several studies focused on discriminative and generative design methods have shown excellent performance beyond the human capability. [18][19][20] Meng et al presented a linear sampling method (LSM) with neural networks to reconstruct the shape of obstacles with acoustic far-field data. [21] LSM relies on selecting a contour line to obtain the shape information of an object.…”

Section: Introductionmentioning

confidence: 99%

A Generative Deep Learning Approach for Shape Recognition of Arbitrary Objects from Phaseless Acoustic Scattering Data

Ahmed

Farhat

Chen

et al. 2023

Advanced Intelligent Systems

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A generative deep learning approach for shape recognition of an arbitrary object from its acoustic scattering properties is proposed and demonstrated. The strategy exploits deep neural networks to learn the mapping between the latent space of a 2D acoustic object and the far‐field scattering amplitudes. A neural network is designed as an adversarial autoencoder and trained via unsupervised learning to determine the latent space of the acoustic object. Important structural features of the object are embedded in lower‐dimensional latent space which supports the modeling of a shape generator and accelerates the learning in the inverse design process. The proposed inverse design uses the variational inference approach with encoder‐ and decoder‐like architecture where the decoder is composed of two pretrained neural networks: the generator and the forward model. The data‐driven framework finds an accurate solution to the ill‐posed inverse scattering problem, where nonunique solution space is overcome by the multifrequency phaseless far‐field patterns. This inverse method is a powerful design tool that doesn't require complex analytical calculation and opens up new avenues for practical realization, automatic recognition of arbitrary‐shaped submarines or large fish, and other underwater applications.

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Section: Introductionmentioning

confidence: 99%

A Generative Deep Learning Approach for Shape Recognition of Arbitrary Objects from Phaseless Acoustic Scattering Data

Ahmed

Farhat

Chen

et al. 2023

Advanced Intelligent Systems

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“…Traditionally, this gap is minimized using empirical trial-and-error methods in conjunction with prior domain knowledge. With technological advancements and increased computational power, efficient optimization algorithms and data-driven techniques, such as machine learning (ML) are employed to automate the learning process while utilizing physics-based understanding (Bacigalupo et al 2020;Donda et al 2021;Ahmed et al 2021;Sun et al 2021;Gurbuz et al 2021;Zheng et al 2020;Wu et al 2021;Bianco et al 2019;Wu et al 2022) through physical models.…”

Section: Introductionmentioning

confidence: 99%

A machine learning accelerated inverse design of underwater acoustic polyurethane coatings

Weeratunge

Shireen

Sagar

et al. 2022

Struct Multidisc Optim

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Here we propose a detailed protocol to enable an accelerated inverse design of acoustic coatings for underwater sound attenuation application by coupling Machine Learning and an optimization algorithm with Finite Element Models (FEM). The FEMs were developed to obtain the realistic performance of the polyurethane (PU) acoustic coatings with embedded cylindrical voids. The frequency dependent viscoelasticity of PU matrix is considered in FEM models to substantiate the impact on absorption peak associated with the embedded cylinders at low frequencies. This has been often ignored in previous studies of underwater acoustic coatings, where usually a constant frequency-independent complex modulus was used for the polymer matrix. The key highlight of the proposed optimization framework for the inverse design lies in its potential to tackle the computational hurdles of the FEM when calculating the true objective function. This is done by replacing the FEM with an efficiently computable surrogate model developed through a Deep Neural Network. This enhances the speed of predicting the absorption coefficient by a factor of $$4.5 \times 10^3$$ 4.5 × 10 3 compared to FEM model and is capable of rapidly providing a well-performing, sub-optimal solution in an efficient way. A significant, broadband, low-frequency attenuation is achieved by optimally configuring the layers of cylindrical voids. This is accomplished by accommodating attenuation mechanisms, including Fabry–P$$\acute{e}$$ e ´ rot resonance and Bragg scattering of the layers of voids. Furthermore, the proposed protocol enables the inverse and targeted design of underwater acoustic coatings through accelerating the exploration of the vast design space compared to time-consuming and resource-intensive conventional trial-and-error forward approaches.

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“…The key distinctions of DDMD against conventional approaches are that (i) DDMD can accommodate domain knowledge (in both dataset and model) with topologically free design variation; (ii) it has little restrictions on analytical formulations of design interest; (iii) some of DDMD enables iteration-free design, which pays off the initial cost of data acquisition and model construction. Capitalizing on the advantages, DDMD has reported a plethora of achievements in recent years from diverse domains [1,8,9,10,12,13,14].…”

Section: Introductionmentioning

confidence: 99%

t-METASET: Tailoring Property Bias of Large-Scale Metamaterial Datasets through Active Learning

Lee¹,

Chan²,

Wei³

et al. 2022

Preprint

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Inspired by the recent success of deep learning in diverse domains, data-driven metamaterials design has emerged as a compelling design paradigm to unlock the potential of multiscale architecture. However, existing model-centric approaches lack principled methodologies dedicated to high-quality data generation. Resorting to space-filling design in shape descriptor space, existing metamaterial datasets suffer from property distributions that are either highly imbalanced or at odds with design tasks of interest. To this end, we propose t-METASET: an intelligent data acquisition framework for task-aware dataset generation. We seek a solution to a commonplace yet frequently overlooked scenario at early design stages: when a massive ( ∼ O(10 4 )) shape library has been prepared with no properties evaluated. The key idea is to exploit a data-driven shape descriptor learned from generative models, fit a sparse regressor as the start-up agent, and leverage diversity-related metrics to drive data acquisition to areas that help designers fulfill design goals. We validate the proposed framework in three hypothetical deployment scenarios, which encompass general use, task-aware use, and tailorable use. Two large-scale shape-only mechanical metamaterial datasets are used as test datasets. The results demonstrate that t-METASET can incrementally grow task-aware datasets. Applicable to general design representations, t-METASET can boost future advancements of not only metamaterials but data-driven design in other domains.

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Generative adversarial networks for the design of acoustic metamaterials

Cited by 79 publications

References 33 publications

A Generative Deep Learning Approach for Shape Recognition of Arbitrary Objects from Phaseless Acoustic Scattering Data

A Generative Deep Learning Approach for Shape Recognition of Arbitrary Objects from Phaseless Acoustic Scattering Data

A machine learning accelerated inverse design of underwater acoustic polyurethane coatings

t-METASET: Tailoring Property Bias of Large-Scale Metamaterial Datasets through Active Learning

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