A Neural Network Framework for Geodesic-Like Curve Computation on Parametric Surfaces

• URL: http://arxiv.org/abs/2606.18759v1
• Date: Wed, 17 Jun 2026 07:18:39 GMT
• Title: A Neural Network Framework for Geodesic-Like Curve Computation on Parametric Surfaces
• Abstract summary: We propose an efficient approach for computing geodesic-like curves by leveraging deep learning and Physics-Informed Neural Networks (PINNs)<n>Under the proposed framework, not only can single parametric surfaces be handled efficiently, but a broad class of complex parametric surfaces can also be robustly addressed.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The concept of geodesic-like curves was introduced by Chen in 2010 as a method for estimating shortest paths (geodesics) on parametric surfaces, with its convergence established theoretically. However, an efficient numerical computational framework has not yet been developed. In this paper, we propose an elegant and efficient approach for computing geodesic-like curves by leveraging deep learning and Physics-Informed Neural Networks (PINNs). Under the proposed framework, not only can single parametric surfaces be handled efficiently, but a broad class of complex parametric surfaces including multi-surface systems with $C^0$ or higher continuity and surfaces of revolution can also be robustly addressed.

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