论文标题
使用$ε$加权的混合查询策略进行回归的深度积极学习
Deep Active Learning for Regression Using $ε$-weighted Hybrid Query Strategy
论文作者
论文摘要
设计一个廉价的近似替代模型，该模型捕获昂贵的高保真行为的显着特征是设计优化的一种方法。最近，深度学习（DL）模型被用作一种有前途的工程问题替代计算模型。但是，创建基于DL的替代物的主要挑战是模拟/标记大量的设计点，这对于计算上的昂贵和/或高维工程问题来说是耗时的。在目前的工作中，我们通过将主动学习（AL）方法与DL相结合，提出了一种新颖的抽样技术。我们称此方法为$ε$ - 加权混合查询策略（$ε$ -HQS），该策略的重点是评估每次学习迭代时替代物的评估，并估算了设计空间中代理的失败概率。通过重复使用已经收集的培训和测试数据，学习的故障概率将下一个迭代的抽样过程引向了高失败概率的区域。在经验评估期间，与其他样本选择方法相比，观察到替代物的精度更好。我们在两个不同的工程设计领域，基于有限元的静态应力分析（计算昂贵的过程）和第二次海底螺旋桨设计（高维问题）中对此方法进行了经验评估。 https://github.com/vardhah/epsilon_weighted_hybrid_query_strategy
Designing an inexpensive approximate surrogate model that captures the salient features of an expensive high-fidelity behavior is a prevalent approach in design optimization. In recent times, Deep Learning (DL) models are being used as a promising surrogate computational model for engineering problems. However, the main challenge in creating a DL-based surrogate is to simulate/label a large number of design points, which is time-consuming for computationally costly and/or high-dimensional engineering problems. In the present work, we propose a novel sampling technique by combining the active learning (AL) method with DL. We call this method $ε$-weighted hybrid query strategy ($ε$-HQS) , which focuses on the evaluation of the surrogate at each learning iteration and provides an estimate of the failure probability of the surrogate in the Design Space. By reusing already collected training and test data, the learned failure probability guides the next iteration's sampling process to the region of the high probability of failure. During the empirical evaluation, better accuracy of the surrogate was observed in comparison to other methods of sample selection. We empirically evaluated this method in two different engineering design domains, finite element based static stress analysis of submarine pressure vessel(computationally costly process) and second submarine propeller design( high dimensional problem). https://github.com/vardhah/epsilon_weighted_Hybrid_Query_Strategy
