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理念（BRITE）枢纽奖将资助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的研究，应用于水生栖息地的恢复，涡轮发电机的优化能源效率，以及心脏病的研究和治疗，从而促进科学的进步，促进国家的繁荣，福利和健康。了解流固相互作用的物理学是取得这一进展的关键先决条件。先进的计算工具发挥着重要的作用，但受到资源和时间的限制，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，但它已经显示出处理概率问题而不是确定性问题的希望，并且通过激发经典计算机的新算法。机器学习已经彻底改变了许多行业，如图像/语音识别，并有望对科学计算产生重大影响。这项研究与旨在提高公众和学生对科学、技术、工程和数学兴趣的活动相结合，包括通过可访问的在线视频、新课程内容和整合本科生参与跨学科研究。本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，为湍流流体-结构相互作用的计算研究做出根本性贡献，从而显著提高正解和反解问题的计算速度和效率。与支持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固相互作用问题重新表述为概率问题。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，开发用于能量收集应用的封闭项的逆发现和流体-结构相互作用的正向和逆预测的科学机器学习技术，并开发用于混合应用的量子准备和量子启发算法。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，并使其能够应用于各种各样的问题，如心血管流动，仿生流动控制和传感，或仿生水生机器人的控制。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.