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The SD2 program aims to develop data-driven methods to accelerate scientific discovery and robust design in domains that lack complete models. Engineers regularly use high-fidelity simulations to create robust designs in complex domains such as aeronautics, automobiles, and integrated circuits. In contrast, robust design remains elusive in domains such as synthetic biology, neuro-computation, cyber, and polymer chemistry due to the lack of high-fidelity models. SD2 will develop tools to enable robust design despite the lack of complete scientific models.

The complexities of brain and behavior pose fundamental questions in many areas of science and engineering, drawing intense interest across a broad spectrum of disciplinary perspectives while eluding explanation by any one of them. Rapid advances within and across disciplines are leading to an increasingly interconnected fabric of theories, models, empirical methods and findings, and educational approaches, opening new opportunities to understand complex aspects of neural and cognitive systems through integrative multidisciplinary approaches.

The National Science Foundation (NSF) seeks to develop and nurture a national innovation ecosystem that builds upon research to guide the output of scientific discoveries closer to the development of technologies, products and processes that benefit society.

The HFSP supports novel, innovative and interdisciplinary basic research focused on the complex mechanisms of living organisms; topics range from molecular and cellular approaches to systems and cognitive neuroscience and the interactions between organisms. A clear emphasis is placed on novel collaborations that bring biologists together with scientists from fields such as physics, mathematics, chemistry, computer science and engineering to focus on problems at the frontier of the life sciences.

EXplainable Artificial Intelligence (XAI) aims to provide strong predictive value along with mechanistic understanding by combining machine learning techniques with effective explanatory techniques. This Funding Opportunity Announcement (FOA) solicits applications in the area of XAI applied to neuroscientific questions of encoding, decoding, and modulation of neural circuits linked to behavior. This FOA encourages collaborations between computationally and experimentally-focused investigators. This FOA seeks machine learning algorithms able to mechanistically explain how experimental manipulations can improve cognitive, affective, or social processing in humans or animals. Proof-of-concept applications aimed at improving the current state of the technology that use XAI to provide unbiased, hierarchical explanations of causal relationships between complex neural and behavioral data are also responsive.