Federal agency name: defense advanced research projects agency (DARPA), defense sciences office (DSO) funding opportunity title: young faculty award (YFA) funding opportunity number:…

Cornell University received a $250,000 cooperative agreement from the Defense Advanced Research Projects Agency (DARPA), Defense Sciences Office, under the Research and Technology Development program (CFDA 12.910), awarded September 30, 2025. Under this Young Faculty Award (YFA) funding opportunity, Cornell will develop a fast multi-domain, multi-objective design optimization technique designed to accelerate the complex system engineering process. The core deliverable is an automated method that enables engineers to efficiently identify optimal component combinations and evaluate tradeoffs between competing design objectives, thereby reducing development timelines while improving solution quality for agile complex system design.

The research addresses critical challenges in developing systems within dynamic operational environments, specifically targeting the component selection problem in engineering design. Rather than requiring manual component identification by domain experts, the automated system will allow engineers to define initial design constraints and constraints between components, after which the system can rapidly populate alternative designs while optimizing across multiple objectives. This approach aims to mitigate obsolescence risk by enabling faster response to changing operational needs and objectives. The award performance period extends through September 29, 2027, with research conducted at Cornell University's Office of Sponsored Programs in New York.

Federal agency name: defense advanced research projects agency (DARPA), defense sciences office (DSO) funding opportunity title: young faculty award (YFA) funding opportunity number: DARPARA2501CATALOG of federal domestic assistance (cfda) number(s): 12.910 research and technology developmentprogram description: DARPA is soliciting innovative research proposals in the areas of interest to the following DARPA technical offices: defense sciences office (DSO), information innovation office (I2O), microsystems technology office (MTO), and the tactical technology office (TTO). Further detail regarding the specific technical areas of interest can be found below under open office topics. Proposed research should investigate innovative approaches that enable revolutionary advances in science, devices, or systems. Specifically excluded is research that primarily results in evolutionary improvements to the existing state of practice. We propose a fast multi-domain, multi-objective design optimization technique to efficientlyexplore optimal tradeoffs for system-level objectives. Our goal is to speed up development whileproducing better, more rational, solutions to enable agile complex system design.specifically, we target the component selection problem in the engineering design process. In contrast to finding viable components by hand, which is tedious and requires expertise, wepropose an automated method that can find optimal component combinations and present thetradeoffs between design objectives to engineers. Deep domain expertise is only required duringthe initial design process, which defines constraints between components, and then our system canquickly populate these designs while optimizing for different objectives. This approach addresses three core issues that arise when building complex systems in adynamic operational environment:1. Needs and objectives change more quickly than the development process, and once newsystems are operational they are already obsolete.2. Technology is rapidly evolving on many fronts, and while more advanced subsystemsshould improve overall performance, complex dependencies require substantialdevelopment cost to update.3. Modern, lean supply chains can be fragile, and IT is important to quickly (RE)optimizesystems for available components. In general, the search space of components is combinatorially large, but we are able to efficientlysolve these problems (and significantly advance the state of the art) by exploiting the fact that (1)many constraint expressions between components and the objectives are monotonic functions (i.e, increasing or decreasing) and (2) non-monotonic elements can be expressed as a constraintsatisfaction problem (CSP), which allows US to exploit advances in this optimization technology.