标题： 基于特征补充的深度神经网络预测微流控藻酸盐微纤维的性能 显示英文标题

标题： Predicting Performance of Microfluidic-Based Alginate Microfibers with Feature-Supplemented Deep Neural Networks

摘要： 用于使用微流控装置制造微纤维的解决方案浓度和流量选择在很大程度上是经验性的试错过程，这主要是由于建模这种多物理场过程的困难。 机器学习，包括深度神经网络，提供了通过使用过去的制造数据来训练和验证模型，从而确定流量和溶液特性的潜力。 不幸的是，微流控技术数据量较小，这可能导致不准确性和过拟合。 为了减少使用深度神经网络开发预测和设计模型时固有的误差，研究了两种方法：利用现有样本的统计特性进行数据集扩展，以及通过引入与物理相关的参数来增强模型，特别是无量纲数，如雷诺数、毛细管数、韦伯数和佩克莱特数。 结果表明，引入这些参数提高了预测能力，从而提高了准确性，而目前在设计参数选择方面尚未观察到类似的改进。 显示英文摘要

摘要： Selection of solution concentrations and flow rates for the fabrication of microfibers using a microfluidic device is a largely empirical endeavor of trial-and-error, largely due to the difficulty of modeling such a multiphysics process. Machine learning, including deep neural networks, provides the potential for allowing the determination of flow rates and solution characteristics by using past fabrication data to train and validate a model. Unfortunately, microfluidics suffers from low amounts of data, which can lead to inaccuracies and overtraining. To reduce the errors inherent with developing predictive and design models using a deep neural network, two approaches are investigated: dataset expansion using the statistical properties of available samples and model enhancement through introduction of physics-related parameters, specifically dimensionless numbers such as the Reynolds, capillary, Weber, and Peclet numbers. Results show that introduction of these parameters provides enhanced predictive capability leading to increased accuracy, while no such improvements are yet observed for design parameter selection.