In this paper, we investigate two deep learning approaches to recovering initial temperature
profiles from thermographic images in non-destructive material testing. First, we trained a …

In this Tutorial, we combine the different scientific fields of information theory,
thermodynamics, regularization theory, and non-destructive imaging, especially for …

We combine three different approaches to greatly enhance the defect reconstruction ability
of active thermographic testing. As experimental approach, laser-based structured …

This study presents an analytical solution-embedded physics informed neural network for
reconstructing three-dimensional temperature fields from sparse discrete data. The Fourier …

The present research proposes a two-step physics-and machine-learning (ML)-based
electromechanical impedance (EMI) measurement data evaluation approach for sandwich …

The authors of this paper propose a deep learning-based tomographic reconstruction
method. Time-of-flight measurements of acoustic waves passing through a region are …

Die aktive Thermografie (TT) nutzt den natürlichen diffusen Wärmefluss, um aus der
gemessenen Oberflächentemperatur Rückschlüsse auf die innere Struktur von technischen …

Thermographic imaging is a contactless and nondestructive way to detect defects inside the
specimen. The current state-of-the-art approach combines model-and deep learning-based …

Thermographic Imaging is a method for the detection of material defects inside the inspected
specimen in a nondestructive manner. U-net was recently proposed to be used for …