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cs.CE2026
Crack detection by holomorphic neural networks and transfer-learning-enhanced genetic optimization
Jonas Hund, Nicolas Cuenca, Tito Andriollo
A physics-informed machine learning framework based on holomorphic neural networks is introduced for detecting cracks in two-dimensional solids from strain or displacement data. Cr…
cs.CE2025
A holomorphic Kolmogorov-Arnold network framework for solving elliptic problems on arbitrary 2D domains
Matteo CalafÃ, Tito Andriollo, Allan P. Engsig-Karup +1
Physics-informed holomorphic neural networks (PIHNNs) have recently emerged as efficient surrogate models for solving differential problems. By embedding the underlying problem str…
cs.CE2024
Physics-Informed Holomorphic Neural Networks (PIHNNs): Solving Linear Elasticity Problems
Matteo CalafÃ, Emil Hovad, Allan P. Engsig-Karup +1
We propose physics-informed holomorphic neural networks (PIHNNs) as a method to solve boundary value problems where the solution can be represented via holomorphic functions. Speci…