An electrical engineering perspective on naturality in computational physics

IF 1.7 3区 数学 Q2 MATHEMATICS, APPLIED Advances in Computational Mathematics Pub Date : 2024-10-01 DOI:10.1007/s10444-024-10197-6
P. Robert Kotiuga, Valtteri Lahtinen
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Abstract

We look at computational physics from an electrical engineering perspective and suggest that several concepts of mathematics, not so well-established in computational physics literature, present themselves as opportunities in the field. We discuss elliptic complexes and highlight the category theoretical background and its role as a unifying language between algebraic topology, differential geometry, and modelling software design. In particular, the ubiquitous concept of naturality is central. Natural differential operators have functorial analogues on the cochains of triangulated manifolds. In order to establish this correspondence, we derive formulas involving simplices and barycentric coordinates, defining discrete vector fields and a discrete Lie derivative as a result of a discrete analogue of Cartan’s magic formula. This theorem is the main mathematical result of the paper.

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从电气工程角度看计算物理学的自然性
我们从电子工程学的角度审视计算物理学,并提出在计算物理学文献中尚未得到广泛认可的几个数学概念,为这一领域带来了机遇。我们讨论了椭圆复数,强调了范畴理论背景及其作为代数拓扑学、微分几何学和建模软件设计之间统一语言的作用。其中,无处不在的自然性概念尤为重要。自然微分算子在三角流形的共链上有类似的函数。为了建立这种对应关系,我们推导出了涉及简约和巴里中心坐标的公式,定义了离散向量场和离散列导数,作为 Cartan 神奇公式离散类比的结果。该定理是本文的主要数学成果。
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来源期刊
CiteScore
3.00
自引率
5.90%
发文量
68
审稿时长
3 months
期刊介绍: Advances in Computational Mathematics publishes high quality, accessible and original articles at the forefront of computational and applied mathematics, with a clear potential for impact across the sciences. The journal emphasizes three core areas: approximation theory and computational geometry; numerical analysis, modelling and simulation; imaging, signal processing and data analysis. This journal welcomes papers that are accessible to a broad audience in the mathematical sciences and that show either an advance in computational methodology or a novel scientific application area, or both. Methods papers should rely on rigorous analysis and/or convincing numerical studies.
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