Project Title: The Rio Grande Consortium for Advanced Research on Exascale Simulation (Grande CARES)

Amount: $5M,

Duration: 2022-2027

Funding agency: DOE NNSA/MSIPP (Grant Number: GRANT13584020)

Project team: UNM - Lead, UTEP, NMSU, NMT, & PVAMU

The Grande CARES brings together six regional institutions: Sandia National Labs (SNL), University of New Mexico (UNM), the University of Texas at El Paso (UTEP), New Mexico State University (NMSU), New Mexico Institute of Mining and Technology (NMT), and Prairie View A&M University (PVAMU). Sandia (an NNSA lab) has world renowned capabilities in advanced Modeling & Simulation (M&S). UNM, UTEP, NMSU, and NMT are four prominent universities and some of the largest Hispanic Serving Institutions in the nation. Our unique focus provides economical solutions to workforce development by promoting advanced degrees in one of the highly sought concentration in computational simulations via cutting-edge research and education. Rio Grande CARES will also produce a sustainable pipeline of scientists and engineers beyond the scope of this project through collaborations established with Sandia scientists and faculty from the partnering institutions. The consortium team aims to address the pressing gap in discipline specific education of students, that often limits their ability to fully explore potential solutions for intricate engineering challenges in large-scale (up to exascale) computational simulations, through an innovative advanced modeling & simulations approach comprising of core physics and crosscutting research thrusts combined with an innovative curriculum. The recruitment focus of the consortium is on scientists and engineers from underrepresented groups, primarily Hispanic students and students with disabilities. Geographic location and cultural settings of the region and easy access to cutting edge NNSA capabilities at Sandia makes the consortium efforts uniquely well-suited to address the MSIPP objectives. The research goal is to develop, validate, and integrate cutting-edge computational tools for complex engineering challenges using high performance computing (HPC), machine learning (ML), data analytics/bigdata concepts, uncertainty quantification, and leading-edge computational capabilities. These will be achieved through comprehensive and fundamental-based M&S integrated with immersive education, strategic curriculum development, targeted internships, and focused collaborative research projects with Sandia scientists. Therefore, the CARES graduates will be invaluable to NNSA with integrated knowledge and skills in advanced M&S for complex engineering applications.