57 citations · 211 across the 18 of their papers we have counts for
4 papers · 1 filter
Hierarchical Bayesian Modeling for Uncertainty Quantification and Reliability Updating using Data
Xinyu Jia, Weinan Hou, Costas Papadimitriou
Quantifying uncertainty and updating reliability are essential for ensuring the safety and performance of engineering systems. This study develops a hierarchical Bayesian modeling…
Navigating Uncertainties in Machine Learning for Structural Dynamics: A Comprehensive Survey of Probabilistic and Non-Probabilistic Approaches in Forward and Inverse Problems
Wang-Ji Yan, Lin-Feng Mei, Jiang Mo +3
In the era of big data, machine learning (ML) has become a powerful tool in various fields, notably impacting structural dynamics. ML algorithms offer advantages by modeling physic…
Physics-informed neural networks for parameter learning of wildfire spreading
Konstantinos Vogiatzoglou, Costas Papadimitriou, Vasilis Bontozoglou +1
Wildland fires pose a terrifying natural hazard, underscoring the urgent need to develop data-driven and physics-informed digital twins for wildfire prevention, monitoring, interve…
Monitoring-Supported Value Generation for Managing Structures and Infrastructure Systems
Antonios Kamariotis, Eleni Chatzi, Daniel Straub +9
To maximize its value, the design, development and implementation of Structural Health Monitoring (SHM) should focus on its role in facilitating decision support. In this position…