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临界效应的小影响的重要工具。该项目旨在创建一个创新的全自动渐近框架，用于研究经典近似方法不可见的现象，使用渐近和数值复分析的新思想。其结果将是第一个可用于数值模拟或真实测量数据的框架，并且可以在没有专家输入的情况下自动应用。它将用于设计水下结构和高效船舶，使表面波的能量损失最小。预期的好处包括使科学家能够获得强大的方法，以及节能工业设计的新途径。