Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics

• URL: http://arxiv.org/abs/2206.01693v1
• Date: Tue, 31 May 2022 13:26:51 GMT
• Title: Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics
• Authors: Alexander Henkes, Henning Wessels
• Abstract summary: This work proposes a generative adversarial network tailored towards three-dimensional microstructure generation. The lightweight algorithm is able to learn the underlying properties of the material from a single microCT-scan without the need of explicit descriptors.
• Score: 77.34726150561087
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multiscale simulations are demanding in terms of computational resources. In the context of continuum micromechanics, the multiscale problem arises from the need of inferring macroscopic material parameters from the microscale. If the underlying microstructure is explicitly given by means of microCT-scans, convolutional neural networks can be used to learn the microstructure-property mapping, which is usually obtained from computational homogenization. The CNN approach provides a significant speedup, especially in the context of heterogeneous or functionally graded materials. Another application is uncertainty quantification, where many expansive evaluations are required. However, one bottleneck of this approach is the large number of training microstructures needed. This work closes this gap by proposing a generative adversarial network tailored towards three-dimensional microstructure generation. The lightweight algorithm is able to learn the underlying properties of the material from a single microCT-scan without the need of explicit descriptors. During prediction time, the network can produce unique three-dimensional microstructures with the same properties of the original data in a fraction of seconds and at consistently high quality.

Related papers

• A Microstructure-based Graph Neural Network for Accelerating Multiscale Simulations [0.0]
  We introduce an alternative surrogate modeling strategy that allows for keeping the multiscale nature of the problem. We achieve this by predicting full-field microscopic strains using a graph neural network (GNN) while retaining the microscopic material model. We demonstrate for several challenging scenarios that the surrogate can predict complex macroscopic stress-strain paths.
  arXiv  Detail & Related papers  (2024-02-20T15:54:24Z)
• Leveraging Frequency Domain Learning in 3D Vessel Segmentation [50.54833091336862]
  In this study, we leverage Fourier domain learning as a substitute for multi-scale convolutional kernels in 3D hierarchical segmentation models. We show that our novel network achieves remarkable dice performance (84.37% on ASACA500 and 80.32% on ImageCAS) in tubular vessel segmentation tasks.
  arXiv  Detail & Related papers  (2024-01-11T19:07:58Z)
• A Neural Network Transformer Model for Composite Microstructure Homogenization [1.2277343096128712]
  Homogenization methods, such as the Mori-Tanaka method, offer rapid homogenization for a wide range of constituent properties. This paper illustrates a transformer neural network architecture that captures the knowledge of various microstructures. The network predicts the history-dependent, non-linear, and homogenized stress-strain response.
  arXiv  Detail & Related papers  (2023-04-16T19:57:52Z)
• Towards Microstructural State Variables in Materials Systems [0.1473281171535445]
  This paper aims to formulate dimensionality and state variable estimation techniques focused on reducing microstructural image data. It is shown that local dimensionality estimation based on nearest neighbors tend to give consistent dimension estimates for natural images for all p-Minkowski distances. It is also shown that stacked autoencoders can reconstruct the generator space of high dimensional microstructural data and provide a sparse set of state variables to fully describe the variability in material microstructures.
  arXiv  Detail & Related papers  (2023-01-11T01:49:18Z)
• Spherical convolutional neural networks can improve brain microstructure estimation from diffusion MRI data [0.35998666903987897]
  Diffusion magnetic resonance imaging is sensitive to the microstructural properties of brain tissue. Estimate clinically and scientifically relevant microstructural properties from the measured signals remains a highly challenging inverse problem that machine learning may help solve. We trained a spherical convolutional neural network to predict the ground-truth parameter values from efficiently simulated noisy data.
  arXiv  Detail & Related papers  (2022-11-17T20:52:00Z)
• A micromechanics-based recurrent neural networks model for path-dependent cyclic deformation of short fiber composites [0.0]
  In this work, a recurrent deep neural network model is trained to predict the path-dependent elasto-plastic stress response of short fiber reinforced composites. The model gives very accurate predictions in a computationally efficient manner when compared with independent micromechanical simulations.
  arXiv  Detail & Related papers  (2022-09-27T12:14:15Z)
• A deep learning driven pseudospectral PCE based FFT homogenization algorithm for complex microstructures [68.8204255655161]
  It is shown that the proposed method is able to predict central moments of interest while being magnitudes faster to evaluate than traditional approaches. It is shown, that the proposed method is able to predict central moments of interest while being magnitudes faster to evaluate than traditional approaches.
  arXiv  Detail & Related papers  (2021-10-26T07:02:14Z)
• ATOM3D: Tasks On Molecules in Three Dimensions [91.72138447636769]
  Deep neural networks have recently gained significant attention. In this work we present ATOM3D, a collection of both novel and existing datasets spanning several key classes of biomolecules. We develop three-dimensional molecular learning networks for each of these tasks, finding that they consistently improve performance.
  arXiv  Detail & Related papers  (2020-12-07T20:18:23Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)
• When Residual Learning Meets Dense Aggregation: Rethinking the Aggregation of Deep Neural Networks [57.0502745301132]
  We propose Micro-Dense Nets, a novel architecture with global residual learning and local micro-dense aggregations. Our micro-dense block can be integrated with neural architecture search based models to boost their performance.
  arXiv  Detail & Related papers  (2020-04-19T08:34:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.