13 citations · 13 across the 2 of their papers we have counts for
3 papers · 1 filter
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…
A holomorphic Kolmogorov-Arnold network framework for solving elliptic problems on arbitrary 2D domains
Matteo Calafà, 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…
Physics-Informed Holomorphic Neural Networks (PIHNNs): Solving Linear Elasticity Problems
Matteo Calafà, 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…