Creating Hybrid Exponential Asymptotics for use with Computational Data

创建用于计算数据的混合指数渐近

• 批准号: DP240101666
• 负责人: Dr Christopher Lustri
• 金额: $ 32.04万
• 依托单位: Macquarie University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2024
• 资助国家: 澳大利亚
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP240101666
• 关键词: Creating; Hybrid; Exponential; Asymptotics; use

Asymptotic analysis is a vital tool for studying small influences with critical effects. This project aims to create an innovative fully-automated asymptotic framework for studying phenomena which are invisible to classical approximation methods, using new ideas from asymptotics and numerical complex analysis. The outcome will be the first framework that can be used on data from numerical simulations or real-life measurements, and which can be applied automatically without hands-on expert input. It will be used to design submerged structures and efficient vessels with minimal energy loss from surface waves. Expected benefits include making powerful methods accessible to scientists, and new paths for energy-efficient industrial design.

渐近分析是研究具有关键影响的小影响的重要工具。该项目旨在使用来自渐近学和数值复杂分析的新思想，创建一个创新的全自动渐近造型框架，用于研究现象，该现象是经典近似方法不可见的。结果将是可以在数值模拟或现实生活中的数据上使用的第一个框架，并且可以自动应用无动手专家输入。它将用于设计淹没的结构和有效的容器，其表面波能量损失最小。预期的好处包括使科学家可以使用强大的方法以及节能工业设计的新途径。