A review of machine learning methods applied to structural dynamics and vibroacoustic
Abstract The use of Machine Learning (ML) has rapidly spread across several fields of
applied sciences, having encountered many applications in Structural Dynamics and …
applied sciences, having encountered many applications in Structural Dynamics and …
Applications of machine learning to wind engineering
Advances of the analytical, numerical, experimental and field-measurement approaches in
wind engineering offers unprecedented volume of data that, together with rapidly evolving …
wind engineering offers unprecedented volume of data that, together with rapidly evolving …
Probabilistic physics-guided machine learning for fatigue data analysis
Abstract A Probabilistic Physics-guided Neural Network (PPgNN) is proposed in this paper
for probabilistic fatigue SN curve estimation. The proposed model overcomes the limitations …
for probabilistic fatigue SN curve estimation. The proposed model overcomes the limitations …
Physics guided neural networks for modelling of non-linear dynamics
The success of the current wave of artificial intelligence can be partly attributed to deep
neural networks, which have proven to be very effective in learning complex patterns from …
neural networks, which have proven to be very effective in learning complex patterns from …
Physics-informed neural networks for solving forward and inverse problems in complex beam systems
This article proposes a new framework using physics-informed neural networks (PINNs) to
simulate complex structural systems that consist of single and double beams based on Euler …
simulate complex structural systems that consist of single and double beams based on Euler …
A machine learning-based surrogate finite element model for estimating dynamic response of mechanical systems
An efficient approach for improving the predictive understanding of dynamic mechanical
system variability is developed in this work. The approach requires low model assessment …
system variability is developed in this work. The approach requires low model assessment …
Interpretable knowledge-guided framework for modeling minimum miscible pressure of CO2-oil system in CO2-EOR projects
Carbon dioxide enhanced oil recovery (CO 2-EOR) is a promising application for carbon
capture, utilization and storage (CCUS). Accurate modeling of CO 2-oil minimum miscible …
capture, utilization and storage (CCUS). Accurate modeling of CO 2-oil minimum miscible …
Artificial intelligence-enhanced seismic response prediction of reinforced concrete frames
H Luo, SG Paal - Advanced Engineering Informatics, 2022 - Elsevier
Existing physics-based modeling approaches do not have a good compromise between
performance and computational efficiency in predicting the seismic response of reinforced …
performance and computational efficiency in predicting the seismic response of reinforced …
Machine learning and physics: A survey of integrated models
A Seyyedi, M Bohlouli, SN Oskoee - ACM Computing Surveys, 2023 - dl.acm.org
Predictive modeling of various systems around the world is extremely essential from the
physics and engineering perspectives. The recognition of different systems and the capacity …
physics and engineering perspectives. The recognition of different systems and the capacity …
Knowledge Integration into deep learning in dynamical systems: an overview and taxonomy
Despite the sudden rise of AI, it still leaves a question mark to many newcomers on its
widespread adoption as it exhibits a lack of robustness and interpretability. For instance, the …
widespread adoption as it exhibits a lack of robustness and interpretability. For instance, the …