☆ 4.7 Article

An introduction to kernel and operator learning methods for homogenization by self-consistent clustering analysis

COMPUTATIONAL MECHANICS (2023)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Review Computer Science, Interdisciplinary Applications

A State-of-the-Art Review on Machine Learning-Based Multiscale Modeling, Simulation, Homogenization and Design of Materials

Dana Bishara et al.

Summary: Multiscale simulation and homogenization are essential computational technologies in material modeling and design, but their adoption in the industrial sector has been limited due to high computational cost. The rapid advancements in artificial intelligence and machine learning have provided new solutions to enhance the computational efficiency and accuracy of multiscale simulations.

ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING (2023)

添加到收藏夹

Article Mathematics, Interdisciplinary Applications

Deep Learning Discrete Calculus (DLDC): a family of discrete numerical methods by universal approximation for STEM education to frontier research

Sourav Saha et al.

Summary: The article proposes the development of Deep Learning Discrete Calculus (DLDC), a set of applied numerical methods that can interpret deep learning algorithms using discrete numerical methods. The DLDC methods aim to utilize the flexibility of deep learning and the resources of numerical analysis to predict unknown system behavior and enhance the speed and accuracy of numerical solutions for known systems. The article is divided into two sections: the first presents the building blocks of DLDC methods and their application in K-12 education, while the second proposes new solution schemes for differential and integral equations in multiscale mechanics.

COMPUTATIONAL MECHANICS (2023)

添加到收藏夹

Article Materials Science, Multidisciplinary

Deep learning prediction of stress fields in additively manufactured metals with intricate defect networks

Brendan P. Croom et al.

Summary: In this study, a deep learning approach is used to predict the elastic stress fields in defect-containing metal microstructures. The trained U-Net model showed higher accuracy and generalizability, providing an efficient way to predict the structural response of defect-containing AM microstructures.

MECHANICS OF MATERIALS (2022)

添加到收藏夹

Article Engineering, Multidisciplinary

Adaptivity for clustering-based reduced-order modeling of localized history-dependent phenomena

Bernardo P. Ferreira et al.

Summary: This article introduces adaptivity in Clustering-based Reduced Order Models (ACROMs) and applies it to Self-Consistent Clustering Analysis (SCA). The Adaptive Self-Consistent Clustering Analysis (ASCA) method improves predictions for materials with history-dependent localization phenomena. The method consists of three main building blocks and proposes solutions to further enhance the adaptive process.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2022)

添加到收藏夹

Article Mathematics, Interdisciplinary Applications

Concurrent n-scale modeling for non-orthogonal woven composite

Jiaying Gao et al.

Summary: This paper adopts a data-driven concurrent n-scale modeling approach (FExSCA(n-1)) to study woven composites, providing an accurate prediction for the structural performance and physics field evolution by reducing the computation of expensive nested composite representative volume element (RVE).

COMPUTATIONAL MECHANICS (2022)

添加到收藏夹

Article

Three ways to solve partial differential equations with neural networks — A review

Jan Blechschmidt et al.

GAMM Mitteilungen (2021)

添加到收藏夹

Article Nanoscience & Nanotechnology

Machine learning based surrogate modeling approach for mapping crystal deformation in three dimensions

Anup Pandey et al.

Summary: The machine learning based surrogate modeling method presented in this study can predict the spatially resolved 3D crystal orientation evolution of polycrystalline materials under uniaxial tensile loading. It is significantly faster than existing crystal plasticity methods and enables simulation of large volumes that would be otherwise computationally prohibitive. This approach surpasses existing ML-based modeling results by providing local 3D full-field predictions rather than just average values or being limited to 2D structures.

SCRIPTA MATERIALIA (2021)

添加到收藏夹

Article Materials Science, Multidisciplinary

Recurrent localization networks applied to the Lippmann-Schwinger equation

Conlain Kelly et al.

Summary: The study introduces a novel machine learning method that combines analytical and data-driven approaches to solve equations in the field of materials science. By using learning-based loop unrolling, the method achieves excellent accuracy in multiscale materials phenomena.

COMPUTATIONAL MATERIALS SCIENCE (2021)

添加到收藏夹

Article Materials Science, Multidisciplinary

Image-based multiscale modeling with spatially varying microstructures from experiments: Demonstration with additively manufactured metal in fatigue and fracture

Orion L. Kafka et al.

Summary: This manuscript introduces a novel modeling framework for predicting mechanical performance in materials with spatially varying microstructure. The method allows for parametric studies exploring the dispersion of voids in additively manufactured metals, and demonstrates its ability to capture variability in part scale performance due to individual microstructures and their distribution. This approach shows promise for application in AM metals.

JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS (2021)

添加到收藏夹

Article Multidisciplinary Sciences

Deep learning model to predict complex stress and strain fields in hierarchical composites

Zhenze Yang et al.

Summary: An AI-based method is developed to predict physical properties of materials directly from their microstructure geometry, bridging the gap between design space and physical performance. The approach shows remarkable accuracy in predicting physical fields and derivative material properties, and offers extensibility in predicting complex materials behavior, improving the efficiency of evaluating physical properties of hierarchical materials.

SCIENCE ADVANCES (2021)

添加到收藏夹

Article Chemistry, Physical

Teaching solid mechanics to artificial intelligence-a fast solver for heterogeneous materials

Jaber Rezaei Mianroodi et al.

Summary: A deep neural network (DNN) is proposed as a fast surrogate model for local stress calculations in inhomogeneous non-linear materials, showing efficient performance and high accuracy in predicting stress distribution. The DNN model is capable of simulating micromechanics in elastic-plastic materials with significant acceleration compared to typical solvers.

NPJ COMPUTATIONAL MATERIALS (2021)

添加到收藏夹

Article Engineering, Manufacturing

Macroscale Property Prediction for Additively Manufactured IN625 from Microstructure Through Advanced Homogenization

Sourav Saha et al.

Summary: This article discusses the use of representative volume element and crystal plasticity material model to predict the mechanical response of metallic parts. The introduction of proper generalized decomposition (PGD) for material model calibration and identification, as well as the proposal of a reduced order method called self-consistent clustering analysis (SCA), were explored during the study. In addition to presenting response analysis, some physical interpretations and assumptions associated with the modeling are also discussed.

INTEGRATING MATERIALS AND MANUFACTURING INNOVATION (2021)

添加到收藏夹

Article Engineering, Manufacturing

Microscale Structure to Property Prediction for Additively Manufactured IN625 through Advanced Material Model Parameter Identification

Sourav Saha et al.

Summary: This article presents the strategy and computational methods for Challenge 4 of the Air Force Research Laboratory additive manufacturing modeling challenge series, where a genetic algorithm-based material model identification method and a proper generalized decomposition (PGD)-based reduced order method are used. The results show that the method is capable of handling large-scale computational problems for local response identification, with promise for using PGD for material model calibration.

INTEGRATING MATERIALS AND MANUFACTURING INNOVATION (2021)

添加到收藏夹

Article Materials Science, Composites

A hierarchical multiscale model for the elastic-plastic damage behavior of 3D braided composites at high temperature

Chunwang He et al.

COMPOSITES SCIENCE AND TECHNOLOGY (2020)

添加到收藏夹

Article Materials Science, Multidisciplinary

Modeling stress-strain curves with neural networks: a scalable alternative to the return mapping algorithm

Maxime Lavech du Bos et al.

COMPUTATIONAL MATERIALS SCIENCE (2020)

添加到收藏夹

Article Engineering, Multidisciplinary

Efficient multiscale modeling for woven composites based on self-consistent clustering analysis

Xinxing Han et al.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2020)

添加到收藏夹

Article Engineering, Multidisciplinary

Adaptive hyper reduction for additive manufacturing thermal fluid analysis

Ye Lu et al.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2020)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

M. Raissi et al.

JOURNAL OF COMPUTATIONAL PHYSICS (2019)

添加到收藏夹

Article Chemistry, Physical

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

Chi Chen et al.

CHEMISTRY OF MATERIALS (2019)

添加到收藏夹

Article Mathematics, Interdisciplinary Applications

Clustering discretization methods for generation of material performance databases in machine learning and design optimization

Hengyang Li et al.

COMPUTATIONAL MECHANICS (2019)

添加到收藏夹

Article Mathematics, Interdisciplinary Applications

Prediction of aerodynamic flow fields using convolutional neural networks

Saakaar Bhatnagar et al.

COMPUTATIONAL MECHANICS (2019)

添加到收藏夹

Article Engineering, Multidisciplinary

Self-consistent clustering analysis for multiscale modeling at finite strains

Cheng Yu et al.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2019)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification

Yinhao Zhu et al.

JOURNAL OF COMPUTATIONAL PHYSICS (2018)

添加到收藏夹

Article Multidisciplinary Sciences

Solving high-dimensional partial differential equations using deep learning

Jiequn Han et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2018)

添加到收藏夹

Article Engineering, Multidisciplinary

Two-scale FE-FFT- and phase-field-based computational modeling of bulk microstructural evolution and macroscopic material behavior

Julian Kochmann et al.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2016)

添加到收藏夹

Article Engineering, Multidisciplinary

Self-consistent clustering analysis: An efficient multi-scale scheme for inelastic heterogeneous materials

Zeliang Liu et al.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2016)

添加到收藏夹

Article Polymer Science

A Multiscale Model for the Quasi-Static Thermo-Plastic Behavior of Highly Cross-Linked Glassy Polymers

N. Vu-Bac et al.

MACROMOLECULES (2015)

添加到收藏夹

Article Biochemical Research Methods

Kernel methods for large-scale genomic data analysis

Xuefeng Wang et al.

BRIEFINGS IN BIOINFORMATICS (2015)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Proper general decomposition (PGD) for the resolution of Navier-Stokes equations

A. Dumon et al.

JOURNAL OF COMPUTATIONAL PHYSICS (2011)

添加到收藏夹

Article Engineering, Multidisciplinary

The LATIN multiscale computational method and the Proper Generalized Decomposition

P. Ladeveze et al.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2010)

添加到收藏夹

Article Engineering, Mechanical

Reduced order modelling method via proper orthogonal decomposition (POD) for flow around an oscillating cylinder

E. Liberge et al.

JOURNAL OF FLUIDS AND STRUCTURES (2010)

添加到收藏夹

Article Materials Science, Multidisciplinary

A multiresolution continuum simulation of the ductile fracture process

Rong Tian et al.

JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS (2010)

添加到收藏夹

Article Materials Science, Multidisciplinary

Multiresolution modeling of ductile reinforced brittle composites

Cahal McVeigh et al.

JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS (2009)

添加到收藏夹

Article Engineering, Multidisciplinary

Linking microstructure and properties through a predictive multiresolution continuum

Cahal McVeigh et al.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2008)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

The reduced model multiscale method (R3M) for the non-linear homogenization of hyperelastic media at finite strains

J. Yvonnet et al.

JOURNAL OF COMPUTATIONAL PHYSICS (2007)

添加到收藏夹

Article Engineering, Multidisciplinary

Multiresolution analysis for material design

Cahal McVeigh et al.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2006)

添加到收藏夹

Article Mechanics

Nonuniform transformation field analysis

JC Michel et al.

INTERNATIONAL JOURNAL OF SOLIDS AND STRUCTURES (2003)

添加到收藏夹

Article Engineering, Multidisciplinary

FE2 multiscale approach for modelling the elastoviscoplastic behaviour of long fibre SiC/Ti composite materials

F Feyel et al.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2000)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.