This project will develop an innovative modeling framework for coupled dynamics of fluid-structure systems based on deep learning, which will contribute new knowledge on rapid modeling of dynamic systems in general. Complex fluids and their interactions with surrounding structures are ubiquitous in natural and industrial processes, e.g., blood flows in compliant vessels, flapping-wing miniature air vehicles, flexible risers in the offshore industry. Predictive modeling of fluid-structure interaction problems is of great significance in numerous engineering applications. However, existing models are primarily based on first-principles methods and numerical discretization techniques, which are computationally expensive and require significant domain expertise. This drawback poses great challenges to real-time predictions (e.g., clinical diagnosis of vascular diseases) and many-query applications (e.g., optimization design of aircraft and uncertainty quantification in high-consequence systems). This grant will support fundamental research on the development of a novel modeling framework by leveraging recent advances in machine learning and prior knowledge of physical principles. This new approach will enable rapid modeling and fast prediction for dynamics of fluid-structure systems, which will have strong practical impacts on a broad spectrum of real-world problems, including cardiovascular diagnosis, aerodynamic design, and active flow control. Therefore, the results of this research can help enhance U.S. healthcare/wellness, national security, and economic competitiveness. Moreover, the multi-disciplinary research topics across physical modeling and artificial intelligence can stimulate interest in the STEM disciplines among young people and thus will have a positive impact on science and engineering education.
Data-based surrogate modeling is a computationally feasible way to tackle fluid-structure interaction problems that require rapid predictions or repeated model evaluations. Deep learning is becoming a popular surrogate modeling approach due to its capability of handling strong nonlinearity and high dimensionality. However, current success of deep learning in the computer science community heavily relies on large-scale labeled data, which are usually not available in the physical modeling community. To address this challenge, this research aims to pioneer a physics-constrained deep learning framework for surrogate modeling of fluid-structure interaction dynamics, which will enable efficient learning with sparse training data. Specifically, a structured deep neural network will be devised to encode the initial and boundary conditions, and the governing equations will be imposed during the training by redesigning the loss (or likelihood) functions to conform to the physics. Numerical experiments of a suite of dynamic fluid-structure interaction problems are planned to answer questions regarding the effect of adding physical constraints in deep learning and their potential in modeling complex physical systems in a parametric setting. This project tackles long-standing difficulties in surrogate modeling of complex dynamical systems with nonlinearity, high-dimensionality, and data scarcity, and contributes to the modeling of dynamical systems in general. The learning framework will bring revolutionary impacts on data-driven surrogate modeling by shifting the paradigm from black-box, data-intensive learning to physics-constrained, data-scarce learning.
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.
