.png){kind=icon}
返回
Bayesian neural networks for uncertainty quantification in data-driven materials modeling
.png){kind=icon}
OA
AI
DOI：10.1016/j.cma.2021.114079.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Modern machine learning (ML) techniques, in conjunction with simulation-based methods, present remarkable potential for various scientific and engineering applications. Within the materials science field, these data-based methods can be used to build efficient structure-property linkages that can be further integrated within multi-scale simulations, or guide experiments in a materials discovery setting. However, a critical shortcoming of state-of-the-art ML techniques is their lack of reliable uncertainty/error estimates, which severely limits their use for materials or other engineering applications where data is often scarce and uncertainties are substantial. This paper presents methods for Bayesian learning of neural networks (NN) that allow consideration of both aleatoric uncertainties that account for the inherent stochasticity of the data-generating process, and epistemic uncertainties, which arise from consideration of limited amounts of data. In particular, algorithms based on approximate variational inference and (pseudo-)Bayesian model averaging achieve an appropriate trade-off between accuracy of the uncertainty estimates and accessible computational cost. The performance of these algorithms is first presented on simple 1D examples to illustrate their behavior in both extrapolation and interpolation settings. The approach is then applied for the prediction of homogenized and localized properties of a composite material. In this setting, data is generated from a finite element model, which permits a study of the behavior of the probabilistic learning algorithms under various amounts of aleatoric and epistemic uncertainties. (C) 2021 Elsevier B.V. All rights reserved.
Keyword:
Data-driven materials modeling
Bayesian neural network
Variational inference
Probabilistic model averaging
Epistemic and aleatoric uncertainties
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.3
论文数:
1.3W
被引数:
5.6W
机构
引用论文
Perspective: Materials informatics and big data: Realization of the fourth paradigm of science in materials science透视: 材料信息学与大数据: 第四科学范式在材料科学中的实现.png)
APL MATERIALSOAAI
APL MATERIALSIF4.5
OAAI分享收藏An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric RetrievalASTRONOMICAL JOURNALOAAI
ASTRONOMICAL JOURNALIF5.1
OAAI分享收藏
Prediction and structural uncertainty analyses of artificial neural networks using hierarchical Bayesian model averagingJOURNAL OF HYDROLOGYOAAI
JOURNAL OF HYDROLOGYIF6.3
OAAI分享收藏Developmental exposure to polychlorinated biphenyls 52, 138 or 180 affects differentially learning or motor coordination in adult rats. mechanisms involvedNeuroscience
NeuroscienceIF0
PREAI
分享收藏Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations物理信息神经网络: 一种用于解决涉及非线性偏微分方程的正反问题的深度学习框架OAAI
OAAI分享收藏Neural Network-Based Uncertainty Quantification: A Survey of Methodologies and Applications基于神经网络的不确定性量化: 方法与应用综述IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容