Expanding the Frontiers of Predictive Simulations of Light and Matter Interactions

扩大光与物质相互作用的预测模拟的前沿

• 批准号: RGPIN-2020-04192
• 负责人: Baranoski, Gladimir
• 金额: $ 2.11万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=716833
• 关键词: Expanding; Frontiers; Predictive; Simulations; Light

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.

自然材料的感知外观在很大程度上取决于它们如何与光相互作用。此外，这些相互作用可以引发能够改变材料的固有特性的复杂现象（例如，颜料含量）以及因此其随时间的外观。因此，这些相互作用是工程和自然科学中广泛调查的焦点，从逼真图像合成中的材料外观建模到应用光学中材料特性的远程评估。 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，无论是在其核心配方和功能方面。为了实现这一目标，它将明确地解决环境刺激，特别是光照，和时间依赖性的材料外观变化之间的因果关系。通过探索这些因果关系，该计划将努力推进材料外观建模和相关动力学现象高保真可视化的最新技术水平。 拟议计划的实施将遵循第一原则方法，重点是将目标因果关系分解为其基本组成部分。这种方法将通过促进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，促进参与该计划的学生之间的合作，并加强他们的分析技能，从而实现创新和强大的仿真算法的设计。此外，将通过一种重视创造性过程中的反复以及对其成果的充分评价和复制的科学方法来实施这一方法。 第一原理推理的这种系统性就业将为学生提供解决问题所需的培训，以成功地解决广泛的技术挑战，在他们的职业生涯。 拟议中的计划将通过直接将材料与周围环境联系起来，使合成图像的保真度更高，特别是在真实的世界的动态模拟方面。此外，它将通过建立一个计算平台，对有关其潜在生物物理机制的假设进行严格和可复制的计算机测试，进一步了解这些联系。 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颠覆性技术，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、非侵入性疾病检测和自然资源的可持续性。因此，拟议的方案也将有助于加拿大努力以具有成本效益的方式解决这些紧迫问题。