A hybrid RNN-GPOD surrogate model for real-time settlement predictions in mechanised tunnelling. - Ba-Trung Cao; Steffen Freitag; Günther Meschke (Adv. Model. Simul. Eng. Sci., 2016)

Artificial neural network surrogate modelling for real-time predictions and control of building damage during mechanised tunnelling. - Ba-Trung Cao; Markus Obel; Steffen Freitag; Peter Mark; Günther Meschke (Adv. Eng. Softw., 2020)

Transfer learning based physics-informed neural networks for solving inverse problems in tunneling. - Chen Xu; Ba-Trung Cao; Yong Yuan; Günther Meschke (CoRR, 2022)

Fusing simulation and monitoring data for real-time settlement prediction during tunnel construction: A multi-fidelity deep operator network (DeepONet). - Chen Xu; Ba-Trung Cao; Yong Yuan; Günther Meschke (CoRR, 2023)

Surrogate modeling for interactive tunnel track design using the cut finite element method. - Hoang-Giang Bui; Ba-Trung Cao; Steffen Freitag; Klaus Hackl; Günther Meschke (Eng. Comput., 2023)

A multi-fidelity deep operator network (DeepONet) for fusing simulation and monitoring data: Application to real-time settlement prediction during tunnel construction. - Chen Xu; Ba-Trung Cao; Yong Yuan; Günther Meschke (Eng. Appl. Artif. Intell., 2024)

Bridging Simulation and Experiment: A Self-Supervised Domain Adaptation Framework for Concrete Damage Classification. - Chen Xu; Giao Vu; Ba-Trung Cao; Zhen Liu; Fabian Diewald; Yong Yuan; Günther Meschke (CoRR, 2025)