Expanding the Frontiers of Predictive Simulations of Light and Matter Interactions

自然材料的外观在很大程度上取决于它们与光的相互作用。此外，这些相互作用可以引起复杂的现象，能够改变材料的内在特征(例如，颜料含量)，从而改变其随着时间的推移的外观。因此，这些相互作用是工程和自然科学中广泛研究的焦点，从真实图像合成中的材料外观建模到应用光学中的材料属性的远程评估。拟议的研究计划旨在推动光和物质相互作用的预测模拟的边界，无论是在它们的核心配方和功能方面。为了实现这一目标，它将明确解决环境刺激，特别是光线暴露与材料外观随时间变化之间的因果关系。通过探索这些因果关系，拟议的计划将努力推动材料外观建模和相关动态现象的高保真可视化方面的最新水平。拟议方案的实施将遵循第一原则方法，以将目标因果关系分解为其基本组成部分。这种方法将有助于将来自不同领域的理论和数据整合到一个全面的知识基础中，促进参与项目的学生之间的合作，并加强他们的分析技能，从而能够设计出创新和健壮的模拟算法。此外，将通过一种科学的方法将这一方法付诸实施，该方法重视创造性过程中的迭代以及对其结果的充分评估和复制。这种对第一原理推理的系统运用将为学生提供解决问题所需的培训，以成功地解决他们职业生涯中广泛的技术挑战。拟议的计划将通过将材料与其周围环境直接联系起来，在合成图像中取得更高水平的保真度，特别是在真实世界的动态描述方面。此外，它将通过建立一个计算平台，对关于其潜在生物物理机制的假说进行严格和可复制的电子测试，从而促进目前对这些联系的理解。计算机科学和生物物理学之间的这种协同作用已被证明有助于开发破坏性技术，以解决影响广大公众和环境的问题。例子包括但不限于提高作物产量、非侵入性检测疾病和自然资源的可持续性。因此，拟议的计划还将有助于加拿大为这些紧迫问题提供具有成本效益的解决方案。

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.