Expanding the Frontiers of Predictive Simulations of Light and Matter Interactions

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The perceived appearance of natural materials is largely determined by how they interact with light. Moreover, these interactions can elicit complex phenomena capable of altering a material's intrinsic characteristics (e.g., pigment content) and, thus, its appearance over time. Accordingly, these interactions are the focus of a wide range of investigations in engineering and natural sciences, from the modeling of material appearance in realistic image synthesis to the remote assessment of material properties in applied optics. The proposed research program aims to push the boundaries of predictive simulations of light and matter interactions, both in terms of their core formulations and functionality. To achieve this goal, it will explicitly address the causal relationships between environmental stimuli, particularly light exposure, and time-dependent material appearance changes. By exploring these causal relationships, the proposed program will strive to advance the state of the art in the modeling of material appearance and the high-fidelity visualization of related dynamical phenomena. The proposed program's implementation will follow a first-principles approach centered at the breaking down of the target causal relationships into their fundamental components. This approach will enable the design of innovative and robust simulation algorithms by facilitating the integration of theory and data from different fields into a comprehensive knowledge foundation, fostering collaborations among the students taking part in the program, and strengthening their analytical skills. Moreover, this approach will be put in practice through a scientific methodology that values iteration in the creative processes as well as the full evaluation and replication of their outcomes. This systematic employment of first-principles reasoning will provide the students with the problem-solving training required to successfully address a broad spectrum of technological challenges during their professional careers. The proposed program will make inroads toward a higher level of fidelity in synthetic imagery, notably with respect to dynamic depictions of the real world, by directly connecting materials with their surrounding environment. Moreover, it will further the current understanding about these connections through the establishment of a computational platform for conducting rigorous and replicable in silico testing of hypotheses regarding their underlying biophysical mechanisms. This synergy between computer science and biophysics has been proved to be instrumental for the development of disruptive technologies to tackle issues affecting the public at large and the environment. Examples include, but are not limited to, the enhancement of crop yield, the noninvasive detection of diseases and the sustainability of natural resources. Hence, the proposed program will also contribute to Canada's efforts towards cost-effective solutions to these pressing issues.