The National Science Foundation awarded a $500,000 Project Grant to the University of California, Berkeley under the Mathematical and Physical Sciences program (CFDA 47.049) to support the development of a bioinspired hierarchical manufacturing process. The two-year award running from November 2021 to October 2023 will fund research into developing stochastic optimization algorithms and software to accelerate the design and simulation of nature-inspired manufacturing techniques. As part of...
The National Science Foundation (NSF) awarded a $271,362 Project Grant to the University of Washington under the Engineering program (CFDA 47.041) with a performance period from January 1, 2024 to June 30, 2025. The grant will fund the development of efficient surrogate modeling methods to predict discontinuous design performance in complex engineering systems, such as smart factories and autonomous material handling systems. The research aims to facilitate the solution of challenging...
This National Science Foundation (NSF) Engineering program (CFDA 47.041) award to Northeastern University for $596,920 aims to develop a mechanism-informed AI platform to enable rapid assembly of flexible and robust biomanufacturing systems. The project will address two key challenges: (1) creating a unified knowledge representation to integrate heterogeneous data across molecular, cellular, and macroscopic scales, and (2) enabling sample-efficient and interpretable learning of fundamental...
This Project Grant from the National Science Foundation's Engineering Directorate (CFDA 47.041) provides $338,078 to Purdue University from October 2021 through September 2024 to advance multifidelity modeling and simulation techniques for engineering design under uncertainty. The award supports the development of novel methods to extract and adapt physics-based information encoded in field responses from computational models of varying fidelity. This will be achieved by combining...
The National Science Foundation awarded a $350,000 Project Grant to the University of Washington under the Mathematical and Physical Sciences federal grant program (CFDA 47.049) to develop novel strategies for constructing optimal statistical estimators using machine learning tools. Over a three-year period ending August 2025, the investigators will study representations of the efficient influence function that can be computed numerically to derive new asymptotically efficient estimators. They...
This CAREER grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) provides $599,972 to Georgia Tech Research Corp to develop a new approach to multifidelity scientific machine learning. The research aims to combine data from high- and low-fidelity computational simulations to create machine-learned models that provide high-accuracy predictions at low computational cost. This will enable more optimal and robust engineering design across domains like space...
This $332,768 National Science Foundation project grant under the Engineering program (CFDA 47.041) will fund the development of a new hybrid pseudo-spectral adjoint algorithm and multidisciplinary design optimization framework at the University of Michigan from September 2022 through August 2025. The algorithm and framework aim to enable the gradient-based MDO of large-scale engineered systems governed by unsteady processes. Specifically, the project will combine time-accurate analysis and...
This $170,000 Project Grant award from the National Science Foundation (NSF) Division of Mathematical Sciences (CFDA 47.049 - Mathematical and Physical Sciences) supports fundamental research on experimental design and uncertainty quantification frameworks for complex systems. The research aims to develop new statistical surrogate models and sequential experimental algorithms to enhance the efficiency and effectiveness of information collection and decision-making for complex systems in...
The National Science Foundation (NSF) Directorate for Engineering (CFDA Program 47.041) awarded a $344,126 Project Grant to the University of Wisconsin System for a 3-year research project focused on developing new mathematical methods and computational tools to enable the use of complex data formats, such as visual and thermal images, for advanced model predictive control (MPC) systems. The key objectives are to: Integrate concepts from control theory, topology, machine learning, and Bayesian...
This five-year, $410,524 National Science Foundation project grant supports research and educational outreach activities aimed at advancing efficient global optimization methods under uncertainty. Funded through NSF's Engineering Directorate under the CFDA 47.041 program, the grantee will develop novel Bayesian optimization algorithms that exploit known problem structures to overcome efficiency barriers in optimizing extremely expensive functions. Specific research goals include optimizing...