This $362,568 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (MPS) program supports the development of advanced computational methods for mechanical systems with imperfect or uncertain geometries. The project aims to transform the computer-aided design (CAD) and analysis cycle by enabling more flexible, automated, and integrated geometric representations and computational analysis.
The grant will fund the application of the Shifted Boundary Method (SBM), an immersed geometry computational technique, combined with probabilistic subdivision surfaces (SSS) to represent geometric uncertainties. This approach is expected to bypass the time-consuming and labor-intensive grid generation process required for digital twins of complex systems. The research will produce an ecosystem of robust and efficient computational methods that can interact with meta-algorithms for digital twin applications, such as reduced-order modeling, machine learning, and optimization. This work aims to help democratize advanced computational capabilities for professionals who are not experts in this field.
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