CyberTraining: Pilot: Employing Proper Orthogonal Decomposition (POD) and High-Performance Computing (HPC) in Advanced CI

Proper Orthogonal Decomposition (POD) is a highly effective, data-driven learning algorithm for solving multi-dimensional Ordinary/Partial Differential Equations (ODEs/PDEs). However, POD is rarely covered in the typical graduate curriculum, and thus the nation is not fully leveraging this advanced algorithm in science and engineering research. To fill this void, this project will conduct a two-week online workshop for trainees in engineering and science-related disciplines and will integrate the developed instructional material into an existing graduate course on High-performance Computing (HPC).

In doing so, this project provides for an educational ecosystem enabling computational and data-driven science for scientists and engineers. By training a diverse group of graduate students, post-docs, and faculty members in various disciplines, this project will help prepare the scientific workforce for advanced CI-enabled research, which will serve to enhance research productivity and enable researchers to effectively address complex societal problems.

The project will help develop the national research workforce in areas of critical need through intensive, integrated instruction on open-source computing platforms to solve ODEs/PDEs by use of POD models that employ HPC. The training will incorporate team-based interdisciplinary projects based on data-driven POD learning algorithm for computationally intensive multiphysics simulation problems in various science and engineering disciplines.

The workshop will provide trainees with intensive instruction on POD and related topics, including open source platforms to solve ODEs/PDEs and eigenvalue problems. Training culminates in a research project where trainees learn advanced computational tools and implementation of HPC skills. The project will broaden the access and adoption of advanced CI while integrating CI skills into existing curriculum models and fostering inter-disciplinary and inter-institutional research collaborations.

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.