This Federal Project Grant award, valued at $313,087.00 and awarded by the National Science Foundation (NSF) under the CFDA 47.041 Engineering program, aims to develop a framework for inverse design and fabrication of multiphase composite materials with tailored mechanical properties. The research will create a physics-informed deep learning (PIDL) model to understand the relationship between material architecture and mechanical behavior, enabling the design of nonlinear materials for applications such as lightweight structures, shock absorbers, and aerospace components. The award will also integrate educational and outreach programs to attract underrepresented groups to engineering and improve data-driven science and engineering learning. The project is a collaborative effort led by the Research Foundation for the State University of New York at Binghamton, with a performance period from November 1, 2024 to October 31, 2027. No subawards are planned under this award.
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