One objective of developing machine learning(ML)-based material models is to integrate them with well-established numerical methods to solve boundary value problems(BVPs).In the family of ML models,recurrent neural ne...One objective of developing machine learning(ML)-based material models is to integrate them with well-established numerical methods to solve boundary value problems(BVPs).In the family of ML models,recurrent neural networks(RNNs)have been extensively applied to capture history-dependent constitutive responses of granular materials,but these multiple-step-based neural networks are neither sufficiently efficient nor aligned with the standard finite element method(FEM).Single-step-based neural networks like the multi-layer perceptron(MLP)are an alternative to bypass the above issues but have to introduce some internal variables to encode complex loading histories.In this work,one novel Frobenius norm-based internal variable,together with the Fourier layer and residual architectureenhanced MLP model,is crafted to replicate the history-dependent constitutive features of representative volume element(RVE)for granular materials.The obtained ML models are then seamlessly embedded into the FEM to solve the BVP of a biaxial compression case and a rigid strip footing case.The obtained solutions are comparable to results from the FEM-DEM multiscale modelling but achieve significantly improved efficiency.The results demonstrate the applicability of the proposed internal variable in enabling MLP to capture highly nonlinear constitutive responses of granular materials.展开更多
针对多层感知机(MLP)架构无法捕获会话序列上下文中的共现关系的问题,提出了一种基于图共现增强MLP的会话推荐模型GCE-MLP。首先,利用MLP架构捕获会话序列的顺序依赖关系,同时通过共现关系学习层获得序列上下文中的共现关系,并通过信息...针对多层感知机(MLP)架构无法捕获会话序列上下文中的共现关系的问题,提出了一种基于图共现增强MLP的会话推荐模型GCE-MLP。首先,利用MLP架构捕获会话序列的顺序依赖关系,同时通过共现关系学习层获得序列上下文中的共现关系,并通过信息融合模块得到会话表示;其次,设计了特定的特征选择层,旨在扩大不同关系学习层输入特征的差异性;最后,通过噪声对比任务最大化两种关系表征之间的互信息,进一步增强对会话兴趣的表征学习。在多个真实数据集上的实验结果表明GCE-MLP的推荐性能优于目前主流的模型,验证了该模型的有效性。与最优的MLP架构模型FMLP-Rec(Filter-enhanced MLP for Recommendation)相比,在Diginetica数据集上,P@20最高达到了54.08%,MRR@20最高达到了18.87%,分别提升了2.14和1.43个百分点;在Yoochoose数据集上,P@20最高达到了71.77%,MRR@20最高达到了31.78%,分别提升了0.48和1.77个百分点。展开更多
基金supported by the National Natural Science Foundation of China(NSFC)(Grant No.12072217).
文摘One objective of developing machine learning(ML)-based material models is to integrate them with well-established numerical methods to solve boundary value problems(BVPs).In the family of ML models,recurrent neural networks(RNNs)have been extensively applied to capture history-dependent constitutive responses of granular materials,but these multiple-step-based neural networks are neither sufficiently efficient nor aligned with the standard finite element method(FEM).Single-step-based neural networks like the multi-layer perceptron(MLP)are an alternative to bypass the above issues but have to introduce some internal variables to encode complex loading histories.In this work,one novel Frobenius norm-based internal variable,together with the Fourier layer and residual architectureenhanced MLP model,is crafted to replicate the history-dependent constitutive features of representative volume element(RVE)for granular materials.The obtained ML models are then seamlessly embedded into the FEM to solve the BVP of a biaxial compression case and a rigid strip footing case.The obtained solutions are comparable to results from the FEM-DEM multiscale modelling but achieve significantly improved efficiency.The results demonstrate the applicability of the proposed internal variable in enabling MLP to capture highly nonlinear constitutive responses of granular materials.
文摘针对多层感知机(MLP)架构无法捕获会话序列上下文中的共现关系的问题,提出了一种基于图共现增强MLP的会话推荐模型GCE-MLP。首先,利用MLP架构捕获会话序列的顺序依赖关系,同时通过共现关系学习层获得序列上下文中的共现关系,并通过信息融合模块得到会话表示;其次,设计了特定的特征选择层,旨在扩大不同关系学习层输入特征的差异性;最后,通过噪声对比任务最大化两种关系表征之间的互信息,进一步增强对会话兴趣的表征学习。在多个真实数据集上的实验结果表明GCE-MLP的推荐性能优于目前主流的模型,验证了该模型的有效性。与最优的MLP架构模型FMLP-Rec(Filter-enhanced MLP for Recommendation)相比,在Diginetica数据集上,P@20最高达到了54.08%,MRR@20最高达到了18.87%,分别提升了2.14和1.43个百分点;在Yoochoose数据集上,P@20最高达到了71.77%,MRR@20最高达到了31.78%,分别提升了0.48和1.77个百分点。
