SBIR Phase I: Near Shore Bathymetry and Coastline Modeling in the Arctic

SBIR第一阶段项目的更广泛影响将是利用人工智能和机器学习(AI/ML)的力量，更好地了解如何为信息不完整的复杂系统构建模型，例如北极的海岸线和近岸。自然海滩过程不是停滞不前的海岸线，而是移动、迁移、填充和侵蚀的动态系统。准确的海底深度和近岸海图对于预测可能影响当地社区、基础设施和航运业的天气、海啸和风暴潮事件非常重要。对于非冰上作业和动态地理需求的民用船舶来说，安全航行的必要性推动了创新建模的需要。传统的测绘技术不能快速、准确地更新不断发展的测深导航模型，以适应商业和社区的需求。AI/ML技术在不同的体系结构、应用和可能的修改方面有很多可以提供和研究的地方。实现当前AI/ML建模的一些最大障碍是，它们需要数据，不能直接包括对系统的当前科学理解，并且被认为是理解系统解决方式的黑匣子。拟议的工作将教AI/ML模型不仅从数据中学习，还从科学理论中学习，然后写出人类可以解释的解决方案。这个SBIR项目将开发一种在动态学习环境中集成数据和模型的方法。当有好的训练数据时，使用AI/ML的系统能够识别非线性、复杂系统中的关键模式。然而，这一过程不会对系统动力学产生新的见解。海滩物理和海岸形态是当今研究的热点，已经产生了高质量的传统海岸模型代码库，既有现象学生成的代码库，也有基于物理的代码库，但这些代码库不易更新。该项目将使用分析功能和数据设计一个体系结构，以开发代码库形式的解决方案，然后可以作为传统的海岸模型进行审查和编译，允许使用新的AI/ML方法，而不需要用户学习新的可读格式。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。

The broader impact of this SBIR Phase I project will be leveraging the power of Artificial Intelligence and Machine Learning (AI/ML) to better understand how to construct models for complicated systems with incomplete information, such as the coastline and nearshore of the Arctic. Natural beach processes are not stagnant shorelines but rather dynamic systems that shift, migrate, fill and erode. Accurate seafloor depths and nearshore charts are important for forecasting weather, tsunami, and storm surge events that can impact local communities, infrastructure, and shipping industries. The necessity of safe navigation for civilian ships not rated for ice operations and dynamic geographical needs is driving the need for innovative modeling. Traditional mapping techniques are incapable of updating constantly evolving bathymetric navigation models quickly and accurately to suit the needs of business and communities. AI/ML technology has much to offer and investigate in terms of different architectures, applications and possible modifications. Some of the largest roadblocks to implementing current AI/ML modeling are that they require data, cannot directly include current scientific understandings of systems, and are considered a black box for understanding the way the system is solved. The proposed work will teach AI/ML models to learn from more than just data but also scientific theories, and then to write out solutions that can be interpreted by humansThis SBIR project will develop a method to integrate data and models in a dynamic learning environment. Systems using AI/ML have the capability to identify critical patterns in nonlinear, complex systems when good training data is available. However, this process does not create new insights on system dynamics. Beach physics and coastal morphology are actively researched today and have led to high quality traditional coastal modeling codebases, both phenomenologically generated and physics-based, but these are not easily updated. This project will design an architecture using analytical functions and data to develop solutions as a codebase, which can then be reviewed and compiled as a traditional coastal model, allowing for novel AI/ML methods without requiring the user to learn new readable formats.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.