The rapid growth of deep learning research, including within the field of computational
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …

Additive manufacturing (AM) has revolutionised the manufacturing world due to its unique
advantages, such as the ability to create complex geometries, work with dissimilar metallic …

In ultrasonic testing, diffraction artifacts generated around defects increase the challenge of
quantitatively characterizing defects. In this paper, we propose a label-enhanced semi …

This study presents an automated technique combining ultrasonic pulse echo method with
machine learning algorithms to detect and classify the depth of interface defects in …

Neural networks have recently gained attention in the context of solving inverse problems.
Physics-Informed Neural Networks (PINNs) are a prominent methodology for the task of …

Recent advances in phased array guided wave (PAGW) have demonstrated the potential of
minor damage detection and localization in widely used curved plates, but quantitative …

Cross-language natural language processing (NLP) presents numerous challenges due to
the wide array of linguistic structures and vocabulary found within each language. Transfer …

Neural networks have recently been employed as material discretizations within adjoint
optimization frameworks for inverse problems and topology optimization. While …

Abstract Natural Language Processing (NLP), specifically, has emerged as a vital weapon
against cybercrime, particularly for network log analysis. As network traffic grows ever more …

Full waveform inversion (FWI) is a powerful tool for reconstructing material fields based on
sparsely measured data obtained by wave propagation. For specific problems, discretizing …