关于全卷积神经网络建模时空演化物理系统的可再现性

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE DataCentric Engineering Pub Date : 2021-05-12 DOI:10.1017/dce.2022.18
Wagner G. Pinto, Antonio Alguacil, M. Bauerheim
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引用次数: 2

摘要

通过在相同条件下(数据库、超参数和硬件)使用不确定的图形处理单元操作对相同网络进行多次训练,评估了深度学习全卷积神经网络的可重复性。该网络被训练成在两个维度上模拟三种典型的时空演化物理系统:热方程、伯格方程和波动方程。在递归和非递归任务上评估网络的行为。可以观察到模型属性(权重和特征字段)的显著变化。当在各种基准上进行测试时,这些模型系统地返回具有高水平偏差的估计,特别是对于由于不确定性而强烈放大可变性的循环分析。用双浮点精度执行的训练提供了稍微更好的估计,并显著减少了网络参数及其测试误差范围的可变性。
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On the reproducibility of fully convolutional neural networks for modeling time–space-evolving physical systems
Abstract Reproducibility of a deep-learning fully convolutional neural network is evaluated by training several times the same network on identical conditions (database, hyperparameters, and hardware) with nondeterministic graphics processing unit operations. The network is trained to model three typical time–space-evolving physical systems in two dimensions: heat, Burgers’, and wave equations. The behavior of the networks is evaluated on both recursive and nonrecursive tasks. Significant changes in models’ properties (weights and feature fields) are observed. When tested on various benchmarks, these models systematically return estimations with a high level of deviation, especially for the recurrent analysis which strongly amplifies variability due to the nondeterminism. Trainings performed with double floating-point precision provide slightly better estimations and a significant reduction of the variability of both the network parameters and its testing error range.
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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