BRITE Pivot: Quantum Computing and Machine Learning for Fluid-Structure Interaction Problems

这个促进工程变革和公平进步的研究思路（BRITE）枢轴奖将资助研究，加速发现涉及流体和固体之间相互作用的重大挑战的解决方案，应用于水生栖息地恢复，优化涡轮机发电机的能源效率，以及心脏病的研究和治疗，从而促进科学进步和促进国家繁荣，福利和健康。理解流体-结构相互作用的物理学是这种进步的关键先决条件。先进的计算工具发挥着重要作用，但受到资源和时间限制的限制，即使在现有的超级计算机上也是如此。该项目将通过开发新的计算工具来解决这些限制，这些工具依赖于最先进的机器学习和量子计算技术，并预计在未来几代量子计算机上实现。量子计算距离实用的基于门的数字量子计算机还有5到10年的时间，但已经显示出在现有量子计算机上处理概率问题而不是确定性问题的希望，并为经典计算机带来了新的算法。机器学习已经彻底改变了图像/语音识别等许多行业，预计将对科学计算产生巨大影响。该研究与旨在提高公众和学生对科学，技术，工程和数学的兴趣的活动相结合，包括通过可访问的在线视频，新的课程内容，以及跨学科研究中的本科生整合。该研究旨在通过从经典方法转向新的计算概念，即量子计算和机器学习，使得正向和反向问题的计算速度和效率都得到显著提高。与支撑量子计算和机器学习的概率观点一致，本研究的一个关键概念是将传统的流体-结构相互作用问题重新定义为概率问题。为此，该项目将把用于湍流建模的过滤密度函数方法扩展到粒子-湍流相互作用的大涡模拟，开发科学机器学习技术，用于封闭项的反向发现以及能量收集应用中流体-结构相互作用的正向和反向预测，并开发用于混合应用的量子就绪和量子启发算法。这些发展将帮助首席研究员获得新的研究工具的专业知识，这些工具有可能导致基础知识的重大进步，并能够应用于各种问题，如心血管流量，生物启发流量控制和传感，该奖项反映了NSF的法定使命，并通过使用基金会的智力价值进行评估，被认为值得支持和更广泛的影响审查标准。

This Boosting Research Ideas for Transformative and Equitable Advances in Engineering (BRITE) Pivot award will fund research that accelerates the discovery of solutions to grand challenges involving interactions between fluids and solids, with applications to aquatic habitat restoration, optimized energy efficiency of turbine generators, and the study and treatment of heart disease, thereby promoting the progress of science and advancing the national prosperity, welfare, and health. Understanding the physics of fluid-structure interactions is a critical prerequisite for such progress. Advanced computational tools play an important role but are limited by resource and time constraints, even on existing supercomputers. This project will address these limitations by developing new computational tools that rely on state-of-the-art machine learning and quantum computing techniques and that also anticipate implementation on future generations of quantum computers. Quantum computing is still five to ten years away from a practical gate-based digital quantum computer but has shown promise for handling probabilistic rather than deterministic problems already on available quantum computers and by inspiring new algorithms for classical computers. Machine learning has revolutionized many industries such as image/speech recognition and is expected to have a great impact on scientific computing. This research is integrated with activities that aim to increase interest in science, technology, engineering, and math among the public and students, including through accessible online videos, new curricular content, and integration of undergraduate students in interdisciplinary research.This research aims to make fundamental contributions to the computational study of turbulent fluid-structure interactions by pivoting from classical approaches to new computing concepts, namely quantum computing and machine learning, enabling significant improvements in computational speed and efficiency for both forward and inverse problems. Consistent with the probabilistic perspectives that underpin both quantum computing and machine learning, a key concept of this research is reformulating traditional fluid-structure interaction problems into probabilistic ones. To this end, this project will extend the filtered density function approach for turbulence modeling to large-eddy simulations of particle-turbulence interactions, develop scientific machine learning techniques for inverse discovery of closure terms and forward and inverse prediction of fluid-structure interactions for energy harvesting applications, and develop quantum-ready and quantum-inspired algorithms for mixing applications. These developments will help the principal investigator gain expertise in novel research tools that have the potential to lead to significant advancement of fundamental knowledge and enable application to problems as diverse as cardiovascular flows, bioinspired flow control and sensing, or control of biomimetic aquatic robots.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.