Project Machine learning for enhanced industrial and biological fluid dynamics Program Mechanical Engineering Undergraduate Research Program Term Summer 2025 Status Closed Research Area Machine Learning, Fluid Dynamics, Mechanical Engineering, Chemical Engineering, Computer Science Description This research will study the integration of Machine Learning (ML) into fluid mechanics research. The project aims to leverage the vast data generated by experiments and simulations to gain deeper insights, predict, and optimize fluid flow systems. The research objectives include: Analyzing the strengths and limitations of ML in the context of fluid mechanics research. Emulate the learning mechanisms employed by biological systems to manipulate fluid flow and translate these insights into novel strategies and optimization algorithms for artificial systems through the application of machine learning and data-driven approaches. Leverage the feature extraction capabilities of autoencoders to develop efficient, lower-dimensional representations of high-fidelity fluid dynamics systems, enabling the creation of compact and computationally affordable reduced-order models for industrial and biological applications. This research has the potential to impact both fundamental scientific understanding and industrial applications of fluid mechanics by establishing ML as a powerful tool in this domain. Supervisor Carlos M Corvalan Mentor Type of work required Develop working computer codes that leverage machine learning to solve engineering problems Websites Qualifications Background in linear algebra, differential equations and scientific computing are desirable Credit Hours 0 Weekly Hours 10 (estimated)