CAREER: HCC: DiTwiST: Enabling Digital Twinning of Nature-Inspired Structural Coloration Taxonomy

About This Grant

By effectively mimicking natural, energy-efficient, nano-structural designs through computer simulations, scientists could fabricate novel optical materials with applications in energy-efficient products and visual computing pipelines. Examples of such natural designs abound, including those found on insect wings, insect-eye lenses, bird feathers, and rock surfaces. However, challenges to this optical biomimicry are compounded by the scale, volume, diversity, and intricate interplay of underlying wave-optical phenomena. Subtle light-matter interactions significantly influence the observable properties of light perceived by human observers and/or man-made sensors. This project will develop an effective and efficient, data-driven computational framework that leverages mathematical simplifications to accurately and affordably model complex light-matter interactions. This work will empower researchers to design, prototype, and fabricate cutting-edge, energy-efficient products for capturing, sensing, and displaying visual information. This work will contribute to advancements in the automobile, military, biomedical, architectural, and art and entertainment industries. Furthermore, this project will create new learning materials, develop framework usage guidelines, and disseminate knowledge through K-12 engagement to foster STEM education among aspiring scientists and visual computing professionals. To achieve these goals, the investigator will develop a computational framework for replicating, designing, and rapidly prototyping nano-structural coloration architectures. The research team will: (a) organize the diverse phenomena of wave-matter interactions through visual characterization, phenomenological understanding, and structural features into a robust operational taxonomy; (b) refine recent advancements in computer graphics rendering techniques with rigorous mathematical derivations; and (c) integrate real-world, large-volume data into the computational process. Developing an operational, data-driven taxonomy will enable the investigator to streamline modeling workflows for future digital twinning efforts in structural coloration. The investigator will derive Wigner distribution-based formulations to model light propagation through heterogeneous media. This approach will unify, compact, and modularize the computational framework. Subsequently, the team will integrate the operational taxonomy parametrically into this unified framework. This integration will facilitate efficient and flexible general emulations of optical biomimicry. Finally, the team will develop efficient methods for adaptive data acquisition in conjunction with iterative and incremental model parameter estimation. The resulting computational framework will enable both forward modeling for emulating nanostructures and inverse modeling for determining the properties of structures based on their observed appearances. This work will significantly advance the field of optical biomimicry by facilitating data-intensive, physics-based modeling and simulations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.