FRG: Collaborative Research: Non-Smooth Geometry, Spectral Theory, and Data: Learning and Representing Projections of Complex Systems

复杂的、随时间演变的系统在自然和社会中无处不在，从地球的天气和气候，到生物分子的功能和动态，以及市场和经济的行为，都有例子。尽管表面上很复杂，但许多这样的系统表现出一种潜在的有组织的结构形式(“积木”)，这种结构的发现将增强我们理解和预测广泛现象的能力。该项目的目标是开发下一代数学和算法工具，这些工具可以利用从实验和观测中获得的大数据集的信息内容来创建复杂系统的连贯表示，并使用这些表示来执行预测，并最终进行控制。这些目标将通过数学技术、将动力系统理论和微分几何与机器学习和数据科学相结合的新方法来实现。新开发的技术将通过与气候动力学、空间物理和凝聚态物理领域的专家合作，在现实世界的问题中进行测试和应用。该项目还将通过培训学生和博士后研究人员以及设计多学科讲座课程，为STEM劳动力和课程发展做出贡献。现代科学方法正在经历一场进化的变革，其中大数据集和机器学习算法有可能在某些复杂现象上超越经典的第一原理方法。为了让这些工具被科学界接受，需要一个严格的数学框架来匹配经典建模方法的可验证性和可量化。最近，一种新的工具被称为扩散预测，它基于可证明相容估计，它学习流形上一大类随机动力系统的未知结构。此外，许多已发表的数值实验结果表明，该框架可以远远超出当前理论的限制。特别是，证据表明，一致性证明可以推广到复杂系统的非自治投影、由非紧算子表示的确定性混沌系统、非光滑区域(如分形吸引子)，甚至度量度量空间上的广义张量。这个项目将对这些问题进行严格的数学统一，导致我们建模和描述复杂系统的能力取得变革性的进步。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Complex, time-evolving systems are ubiquitous in nature and society, with examples ranging from the Earth's weather and climate, to the function and dynamics of biomolecules, and the behavior of markets and economies. Despite their apparent complexity, many such systems exhibit a form of underlying organized structure (``building blocks''), whose discovery would enhance our ability to understand and predict a wide range of phenomena. The goal of this project is to develop the next generation of mathematical and algorithmic tools that can harness the information content of large datasets acquired from experiments and observations to create coherent representations of complex systems, and use these representations to perform prediction, and ultimately, control. These objectives will be addressed through a novel combination of mathematical techniques, bridging dynamical systems theory and differential geometry with machine learning and data science. The newly developed techniques will be tested and applied in real-world problems through collaboration with domain experts in the areas of climate dynamics, space physics, and condensed matter physics. The project will also contribute to STEM workforce and curricular development through training of students and postdoctoral researchers, and design of multi-disciplinary lecture courses. The modern scientific method is undergoing an evolutionary change wherein large data sets and machine learning algorithms have the potential to outperform classical first-principles approaches for certain complex phenomena. For these tools to be accepted by the scientific community, a rigorous mathematical framework is required to match the verifiability and quantifiability of the classical modeling approach. Recently, a new tool called the diffusion forecast has been developed based on provably consistent estimators, which learn the unknown structure of a large class of stochastic dynamical systems on manifolds. Moreover, the results of many published numerical experiments indicate that this framework can be applied far beyond the restricted context of the current theory. In particular, the evidence suggests that the consistency proofs can be extended to non-autonomous projections of complex systems, deterministic chaotic systems represented by non-compact operators, non-smooth domains such as fractal attractors, and even generalized tensors on metric-measure spaces. This project will undertake a rigorous mathematical unification of these problems, leading to transformative advances in our ability to model and describe complex systems.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.