4.7 Article

SAP-Net: Deep learning to predict sound absorption performance of metaporous materials

Related references

Note: Only part of the references are listed.
Article Physics, Applied

A predictive deep-learning approach for homogenization of auxetic kirigami metamaterials with randomly oriented cuts

Tongwei Liu et al.

Summary: This paper presents a data-driven approach to predict mechanical properties of auxetic kirigami metamaterials, using finite element method to generate datasets, convolutional neural network to train these data, and establishing an implicit mapping between input orientations of cuts and output Young's modulus and Poisson's ratio. The study shows that a good prediction can be achieved when the distributions of training and test datasets are close to each other.

MODERN PHYSICS LETTERS B (2021)

Article Materials Science, Multidisciplinary

Machine learning-assisted discovery of strong and conductive Cu alloys: Data mining from discarded experiments and physical features

Qingkun Zhao et al.

Summary: An innovative method based on machine learning was proposed to design high-performance copper alloys, addressing the limitations of traditional alloy design methods in terms of cost and time efficiency. The method successfully discovered high-performance copper alloys, demonstrating great potential for developing advanced materials.

MATERIALS & DESIGN (2021)

Article Materials Science, Multidisciplinary

Prediction of diffusion coefficients in fcc, bcc and hcp phases remained stable or metastable by the machine-learning methods

Zhenbang Wei et al.

Summary: This study developed a diffusion activate energy model based on machine-learning methods, starting with the establishment of T-m and C-ij models, followed by the development of a Q model and feature selection to optimize the model. The predictive ability and goodness of fit of the models were successfully validated in the study.

MATERIALS & DESIGN (2021)

Article Acoustics

Directional quantification of power dissipation in sound-absorbing metaporous layers

Jun Hyeong Park et al.

Summary: The insertion of rigid inclusions into metaporous layers can greatly enhance sound absorption by generating multidirectional effects to dissipate sound power. Quantitative studies on the directional characteristics of sound power dissipation in metaporous layers are relatively rare, highlighting the importance of directional quantification for better designs and performance interpretation.

JOURNAL OF SOUND AND VIBRATION (2021)

Article Materials Science, Multidisciplinary

Accelerated design of architectured ceramics with tunable thermal resistance via a hybrid machine learning and finite element approach

E. Fatehi et al.

Summary: The use of machine learning in designing architectured ceramics can improve efficiency and performance, resulting in increased frictional energy dissipation, reduced sliding distance, lowered strain energy, higher safety factor, and delayed structural failure.

MATERIALS & DESIGN (2021)

Article Materials Science, Multidisciplinary

Inverse machine learning framework for optimizing lightweight metamaterials

Adithya Challapalli et al.

Summary: Inverse machine learning is an emerging approach for exploring and optimizing structural designs, with the proposed framework using generative adversarial networks resulting in the discovery of superior lattice unit cells compared to traditional designs.

MATERIALS & DESIGN (2021)

Article Engineering, Mechanical

Hybrid composite meta-porous structure for improving and broadening sound absorption

Nansha Gao et al.

Summary: This study proposes a composite meta-porous structure to improve the sound absorption performance of porous materials, and finds that the hybrid structure with periodic boundary exhibits high sound absorption in a certain frequency range, validated through theoretical models and finite element methods. The research also shows that different boundaries have varying effects on the sound absorption performance.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2021)

Article Instruments & Instrumentation

Ultrathin acoustic absorbing metasurface based on deep learning approach

Krupali Donda et al.

Summary: This paper introduces a deep learning-based approach to simplify the modeling process of acoustic metasurface absorbers while maintaining accuracy. Through convolutional neural networks, wide absorption spectrum response can be simulated within milliseconds. This method is attractive for applications requiring fast on-demand design and optimization of metasurface acoustic absorbers.

SMART MATERIALS AND STRUCTURES (2021)

Article Materials Science, Multidisciplinary

Accelerated topological design of metaporous materials of broadband sound absorption performance by generative adversarial networks

Hongjia Zhang et al.

Summary: The article introduces the research on topological design of metaporous materials for sound absorption using Generative Adversarial Networks (GANs), successfully proposing efficient designs trained with numerical data and greatly accelerating the design process. GANs not only provide more structural and configuration choices, but also generate creative designs and rich local features.

MATERIALS & DESIGN (2021)

Article Materials Science, Multidisciplinary

Accelerating Auxetic Metamaterial Design with Deep Learning

Jackson K. Wilt et al.

ADVANCED ENGINEERING MATERIALS (2020)

Article Multidisciplinary Sciences

Improved protein structure prediction using potentials from deep learning

Andrew W. Senior et al.

NATURE (2020)

Article Engineering, Mechanical

A machine learning -based method to design modular metamaterials

Lingling Wu et al.

EXTREME MECHANICS LETTERS (2020)

Article Mechanics

A double porosity material for low frequency sound absorption

Honggang Zhao et al.

COMPOSITE STRUCTURES (2020)

Article Physics, Applied

Inverse design of acoustic metamaterials based on machine learning using a Gauss-Bayesian model

Bin Zheng et al.

JOURNAL OF APPLIED PHYSICS (2020)

Article Materials Science, Multidisciplinary

Deep learning for topology optimization of 2D metamaterials

Hunter T. Kollmann et al.

MATERIALS & DESIGN (2020)

Article Materials Science, Multidisciplinary

Using deep neural network with small dataset to predict material defects

Shuo Feng et al.

MATERIALS & DESIGN (2019)

Article Materials Science, Multidisciplinary

Deep learning based predictive modeling for structure-property linkages

Anuradha Beniwal et al.

MATERIALIA (2019)

Article Materials Science, Multidisciplinary

Material structure-property linkages using three-dimensional convolutional neural networks

Ahmet Cecen et al.

ACTA MATERIALIA (2018)

Article Materials Science, Multidisciplinary

Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets

Zijiang Yang et al.

COMPUTATIONAL MATERIALS SCIENCE (2018)

Article Computer Science, Interdisciplinary Applications

Prediction of transmission, reflection and absorption coefficients of periodic structures using a hybrid Wave Based - Finite Element unit cell method

Elke Deckers et al.

JOURNAL OF COMPUTATIONAL PHYSICS (2018)

Article Multidisciplinary Sciences

Deep neural networks for accurate predictions of crystal stability

Weike Ye et al.

NATURE COMMUNICATIONS (2018)

Article Multidisciplinary Sciences

Machine learning plastic deformation of crystals

Henri Salmenjoki et al.

NATURE COMMUNICATIONS (2018)

Article Physics, Multidisciplinary

Machine learning phases of matter

Juan Carrasquilla et al.

NATURE PHYSICS (2017)

Article Acoustics

Using simple shape three-dimensional rigid inclusions to enhance porous layer absorption

J. -P. Groby et al.

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA (2014)

Article Acoustics

Absorption of a rigid frame porous layer with periodic circular inclusions backed by a periodic grating

J. -P. Groby et al.

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA (2011)