Fan D, Jodin G, Consi T R, Bonfiglio L, Ma Y, Keyes L R, Karniadakis G E, Triantafyllou M S
Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
MIT Sea Grant College Program, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Sci Robot. 2019 Nov 27;4(36). doi: 10.1126/scirobotics.aay5063.
We describe the development of the Intelligent Towing Tank, an automated experimental facility guided by active learning to conduct a sequence of vortex-induced vibration (VIV) experiments, wherein the parameters of each next experiment are selected by minimizing suitable acquisition functions of quantified uncertainties. This constitutes a potential paradigm shift in conducting experimental research, where robots, computers, and humans collaborate to accelerate discovery and to search expeditiously and effectively large parametric spaces that are impracticable with the traditional approach of sequential hypothesis testing and subsequent train-and-error execution. We describe how our research parallels efforts in other fields, providing an orders-of-magnitude reduction in the number of experiments required to explore and map the complex hydrodynamic mechanisms governing the fluid-elastic instabilities and resulting nonlinear VIV responses. We show the effectiveness of the methodology of "explore-and-exploit" in parametric spaces of high dimensions, which are intractable with traditional approaches of systematic parametric variation in experimentation. We envision that this active learning approach to experimental research can be used across disciplines and potentially lead to physical insights and a new generation of models in multi-input/multi-output nonlinear systems.
我们描述了智能拖曳水池的开发情况，这是一种由主动学习引导的自动化实验设施，用于进行一系列涡激振动（VIV）实验，其中下一个实验的参数通过最小化量化不确定性的合适采集函数来选择。这构成了实验研究中的一种潜在范式转变，即机器人、计算机和人类协作以加速发现，并快速有效地搜索传统顺序假设检验及后续试错执行方法难以处理的大型参数空间。我们描述了我们的研究如何与其他领域的努力并行，在探索和绘制控制流体弹性不稳定性及由此产生的非线性VIV响应的复杂流体动力学机制所需的实验数量上实现了数量级的减少。我们展示了“探索与利用”方法在高维参数空间中的有效性，而传统的实验系统参数变化方法难以处理这些高维参数空间。我们设想，这种实验研究的主动学习方法可跨学科使用，并可能在多输入/多输出非线性系统中带来物理见解和新一代模型。
