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arXiv 2310.02935math.AP
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分段非线性材料与单调性原理

Piecewise nonlinear materials and Monotonicity Principle

Antonio Corbo Esposito, Luisa Faella, Vincenzo Mottola, Gianpaolo Piscitelli, Ravi Prakash, Antonello Tamburrino

AI总结:

针对含分段增长指数的多非线性材料的单调性原理问题,借助平均Dirichlet-to-Neumann算子拓展方法普适性,可建模多种实际材料构型,经数值验证为相关快速成像方法应用提供理论支撑。

摘要:

本文聚焦于具有分段增长指数的非线性材料的单调性原理(Monotonicity Principle,MP)。相关研究结果具有重要意义,因为它们使得基于MP的快速成像方法可应用于包含两种及以上材料(其中至少一种为非线性材料)的广泛问题类别。本文的处理方法极具普适性,可对多种实际构型进行建模,例如超导(Superconducting,SC)材料、理想导电(Perfect Electrical Conducting,PEC)材料或理想绝缘(Perfect Electrical Insulating,PEI)材料。平均Dirichlet-to-Neumann算子发挥了关键作用,该算子由Corbo Esposito等人于2021年发表在《Inverse Problems》的论文中提出,当时该研究仅处理了单一类型非线性的MP问题。真实数值算例验证了本文的理论结论。

英文摘要:

This paper is focused on the Monotonicity Principle (MP) for nonlinear materials with piecewise growth exponent. This results are relevant because enables the use of a fast imaging method based on MP, to the wide class of problems with two or more materials, where at least one is nonlinear. The treatment is very general and allows to model a wide variety of practical configurations such as, for instance, Superconducting (SC) or Perfect Electrical Conducting (PEC) or Perfect Electrical Insulating (PEI) materials. A key role is played by the average Dirichlet-to-Neumann operator, introduced in [Corbo Esposito et. al, Inverse Problems 2021], where the MP for a single type of nonlinearity was treated. Realistic numerical examples confirm the theoretical findings.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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