Center for Scientific Machine Learning for Material Sciences
Abstract
The proposed Center for Scientific Machine Learning for Material Sciences aims to bring together a multidisciplinary team of experts in applied mathematics, physics, statistics, optimization, and machine learning, in collaboration with materials scientists. The primary objective of the center is to develop a foundational SciML framework that will drive advancements in materials design and discovery. This comprehensive framework will encompass uncertainty quantification, predictive simulation, and optimization tools. By focusing on the Electron beam powder bed fusion (EBPBF) platform, the center will leverage the inherent equivalence between electron beam processing and scanning electron microscopy, utilizing sensor data to facilitate data-driven and principle-guided scientific exploration. The research activities will involve the development of mathematical models such as Bayesian neural networks and sparsified SciML models, with the potential for broader application in various fields. Additionally, the center places a strong emphasis on knowledge development and building diversity in the scientific community, fostering the growth of SciML and Data Science programs in Historically Black Colleges and Universities (HBCUs) and Minority-Serving Institutions (MSIs), and actively involving undergraduate and graduate students in cutting-edge research. The impact of this research extends beyond academic boundaries, empowering materials scientists, driving innovation in diverse fields, and opening new possibilities for advancements across industries.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 14, 2024
- Source ID
- FA95502310725
Entities
People
- Yunjiao Wang
Organizations
- Air Force Office of Scientific Research
- Texas Southern University
- United States Air Force