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: 459970814
• 负责人: Dr.-Ing. Henning Bonart
• 金额: --
• 依托单位: Fachgebiet Nano- und Mikrofluidik
• 依托单位国家: 德国
• 项目类别: WBP Position
• 财政年份: 2021
• 资助国家: 德国
• 起止时间: 2020-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/459970814?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.

微流体涉及非常小的液体体积和几何形状，并且可以采用有希望的应用，包括用于药物发现的实验室和液体注入液体的表面（LIS）。但是，通常在统计上量化了测量数据和数学模型以及得出的结论和决策的不确定性以及得出的结论和决策。在贝叶斯不确定性定量（UQ）中，数据和模型是系统地集成的。它在广泛的范围内表现出了巨大的潜力，包括天气和选举预测。不幸的是，它仍然很少应用于微流体学。因此，该项目的目的是表明贝叶斯uq为涉及微流体学的研究和应用提供了很大的好处，并且在实际上和计算上即使在复杂的物理现象上也是可行的。因此，使用了两个有趣且相关的测试案例，其中涉及小规模流体力学，一种基础研究和一种应用程序，用于将复杂的贝叶斯方法应用于微流体实验的嘈杂和不确定的数据中。在第一种情况下，将开发并使用贝叶斯模型比较来定量评估薄膜中孔孔动力学的预测降低阶模型。最终的模型将增强我们对孔动力学的理解，并有助于LIS的设计。在第二种情况下，将开发出通过同骨噬菌体传输的微通道中低浓度的样品的自动检测方案。复杂的检测器将与贝叶斯统计数据集成，以允许在不确定性下进行可追溯和透明的决策。因此，这将允许自动使用微流体检测方案，例如在医学诊断或高通量筛查应用中。为了允许其他研究人员迅速将贝叶斯uq的应用方法转移到其特定的微流体问题和应用中，将提供教程案例以及概念证明。这个高度的跨学科项目结合了流体动态实验，数学建模和贝叶斯统计，以为微流体问题提供新颖而可靠的答案。该项目的结果可能允许贝叶斯uq的更多涉及的应用。例如，一个有趣的问题是定量比较，并将基于物理的模型以及数据驱动的模型或机器学习模型用于复杂的微流体学。正如该项目中将应用的贝叶斯大学的方法为这项未来的研究提供了坚实的基础。