Computable Coordinate System Objects: Theory and Applications

Coordinate system transformations are fundamental in modern mathematics, physics, and engineering. Traditional approaches rely on matrix operations and tensor analysis: the former lacks geometric intuition, while the latter is overly abstract and challenging for numerical implementation. This paper proposes a specialized framework called Computable Coordinate Systems, which discretizes continuous transformations through Lie group-Lie algebra theory, achieving complete algebraic operability of coordinate systems. This paper systematically presents the comprehensive theoretical foundation of computable coordinate systems, including: (1) algebraic structure design based on group theory, defining operations such as multiplication, division, and exponential mapping; (2) Lie group-Lie algebra correspondence, establishing bridges between infinitesimal and finite transformations; (3) Berry connection and curvature theory, providing computable expressions for covariant derivatives and Riemann curvature tensors, correcting curvature formula errors in early research; (4) complete C++ implementation, including data structure design, operator overloading, and numerical algorithms. The numerical validation section demonstrates theoretical correctness through three typical application scenarios: (1) Computational Geometry surface operators, achieving <2% curvature error with O(h^2) convergence; (2) CAD topology verification, achieving 100% accuracy with <1% computational overhead; (3) Fourier spectral transformation, achieving <3.5% reconstruction error with O(h²) convergence rate. Experimental results show that computable coordinate systems significantly simplify implementation complexity while maintaining mathematical rigor, providing efficient and reliable computational tools for engineering applications.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-10-22
DOI
https://doi.org/10.5281/zenodo.17413485
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
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article

Computable Coordinate System Objects: Theory and Applications

Zenodo (CERN European Organization for Nuclear Research)
Model Reduction and Neural Networks
article

Computable Coordinate System Objects: Theory and Applications

article en

Abstract

Coordinate system transformations are fundamental in modern mathematics, physics, and engineering. Traditional approaches rely on matrix operations and tensor analysis: the former lacks geometric intuition, while the latter is overly abstract and challenging for numerical implementation. This paper proposes a specialized framework called Computable Coordinate Systems, which discretizes continuous transformations through Lie group-Lie algebra theory, achieving complete algebraic operability of coordinate systems. This paper systematically presents the comprehensive theoretical foundation of computable coordinate systems, including: (1) algebraic structure design based on group theory, defining operations such as multiplication, division, and exponential mapping; (2) Lie group-Lie algebra correspondence, establishing bridges between infinitesimal and finite transformations; (3) Berry connection and curvature theory, providing computable expressions for covariant derivatives and Riemann curvature tensors, correcting curvature formula errors in early research; (4) complete C++ implementation, including data structure design, operator overloading, and numerical algorithms. The numerical validation section demonstrates theoretical correctness through three typical application scenarios: (1) Computational Geometry surface operators, achieving <2% curvature error with O(h^2) convergence; (2) CAD topology verification, achieving 100% accuracy with <1% computational overhead; (3) Fourier spectral transformation, achieving <3.5% reconstruction error with O(h²) convergence rate. Experimental results show that computable coordinate systems significantly simplify implementation complexity while maintaining mathematical rigor, providing efficient and reliable computational tools for engineering applications.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 47%
Model Reduction and Neural Networks
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