Bayesian Uncertainty Quantification for Microfluidics: Assessing and Improving the Reliability of Reduced-Order Models and Sample Detection Schemes

微流体的贝叶斯不确定性量化：评估和提高降阶模型和样品检测方案的可靠性

• 批准号: 459970841
• 负责人: Dr.-Ing. Henning Bonart
• 金额: --
• 依托单位: Department of Aeronautics and Astronautics
• 依托单位国家: 德国
• 项目类别: WBP Fellowship
• 财政年份: 2021
• 资助国家: 德国
• 起止时间: 2020-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/459970841?language=en
• 关键词: Bayesian; Uncertainty; Quantification; Microfluidics; Assessing

Microfluidics deals with very small fluid volumes and geometries and enables promising applications including lab-on-a-chips for drug discovery and liquid-infused surfaces (LIS) in medicine. Often however, the uncertainty in the measurement data and mathematical models, as well as the drawn conclusions and decisions, are not statistically quantified. In Bayesian uncertainty quantification (UQ), data and models are systematically integrated. It has demonstrated tremendous potential in a broad range including weather and election prediction. Unfortunately, it is still seldom applied in microfluidics.The goal of this project is therefore to show that Bayesian UQ offers a great benefit for research and applications involving microfluidics, and is practically and computationally feasible even for complex physical phenomena. Therefore, two interesting and relevant test cases involving small scale fluid mechanics, one basic research and one application case, are used to apply sophisticated Bayesian methods to noisy and uncertain data from microfluidic experiments. In the first case, a predictive reduced-order model for the dynamics of holes in thin liquid films on vibrating surfaces will be developed and quantitatively assessed using Bayesian model comparison. The resulting model will enhance our understanding of the hole dynamics and help in the design of LIS. In the second case, an automatized detection scheme for samples at low concentration from noisy measurement data in microchannels transported via isotachophoresis will be developed. A sophisticated detector will be integrated with Bayesian statistics to allow for traceable and transparent decisions under uncertainty. Consequently, this then will allow the automatized usage of microfluidic detection schemes, for example in medical diagnostics or high-throughput screening applications. To allow other researchers to rapidly transfer the applied methods of Bayesian UQ to their specific microfluidic problems and applications, tutorial cases as well as proof-of-concepts will be provided. This highly interdisciplinary project combines fluid dynamic experiments, mathematical modeling, and Bayesian statistics to provide novel and reliable answers to microfluidic problems. The results of this project might allow for even more involved applications of Bayesian UQ. For example, one interesting question would be to quantitatively compare, and combine, physic-based models and data-driven or machine learning models for complex microfluidics. Methods from Bayesian UQ, as will be applied in this project, provide a solid foundation for this future research.

微流控技术处理非常小的流体体积和几何形状，并使其具有广阔的应用前景，包括用于药物发现的芯片实验室和医学中的液体注入表面(LI)。然而，测量数据和数学模型中的不确定性以及得出的结论和决定往往没有统计上的量化。在贝叶斯不确定性量化(UQ)中，数据和模型是系统集成的。它已经在包括天气和选举预测在内的广泛范围内展示了巨大的潜力。不幸的是，它在微流体中的应用仍然很少。因此，本项目的目标是表明贝叶斯UQ为涉及微流体的研究和应用提供了巨大的好处，并且即使对于复杂的物理现象，贝叶斯UQ在实践和计算上也是可行的。因此，我们使用两个有趣且相关的小尺度流体力学测试案例，一个基础研究案例和一个应用案例，将复杂的贝叶斯方法应用于来自微流体实验的噪声和不确定数据。在第一种情况下，将建立振动表面薄层液膜中孔洞动力学的预测降阶模型，并通过贝叶斯模型比较对其进行定量评估。所得到的模型将加深我们对孔洞动力学的理解，并有助于LIS的设计。在第二种情况下，将开发一种从通过等速电泳法传输的微通道中的噪声测量数据中自动检测低浓度样品的方案。先进的探测器将与贝叶斯统计相结合，以便在不确定的情况下作出可追踪和透明的决定。因此，这将允许自动使用微流控检测方案，例如在医疗诊断或高通量筛查应用中。为了使其他研究人员能够快速地将贝叶斯UQ的应用方法应用到他们特定的微流体问题和应用中，将提供教程案例以及概念验证。这个高度跨学科的项目结合了流体动力学实验、数学建模和贝叶斯统计，为微流体问题提供了新颖和可靠的答案。这个项目的结果可能允许贝叶斯UQ的更复杂的应用。例如，一个有趣的问题将是对复杂微流体的基于物理的模型和数据驱动或机器学习模型进行定量比较和组合。贝叶斯统一量化方法将在本项目中得到应用，为以后的研究提供了坚实的基础。