57 citations · 211 across the 18 of their papers we have counts for
19 papers
A geometry-aligned multi-fidelity framework for uncertainty quantification of wildfire spread
Konstantinos Vogiatzoglou, Costas Papadimitriou, Vasilis Bontozoglou +2
Forward propagation of input uncertainties in physics-based wildfire models is computationally prohibitive, limiting the use of high-fidelity simulators in risk assessment workflow…
Bayesian Inference for PDE-based Inverse Problems using the Optimization of a Discrete Loss
Lucas Amoudruz, Sergey Litvinov, Costas Papadimitriou +1
Inverse problems are crucial for many applications in science, engineering and medicine that involve data assimilation, design, and imaging. Their solution infers the parameters or…
Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends
Wang-Ji Yan, Lin-Feng Mei, Yuan-Wei Yin +4
Bayesian learning has emerged as a compelling and vital research direction in the field of structural dynamics, offering a probabilistic lens to understand and refine the analysis…
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…
An interpretable wildfire spreading model for real-time predictions
Konstantinos Vogiatzoglou, Costas Papadimitriou, Konstantinos Ampountolas +3
Forest fires pose a natural threat with devastating social, environmental, and economic implications. The rapid and highly uncertain rate of spread of wildfires necessitates a trus…
On the Integration of Physics-Based Machine Learning with Hierarchical Bayesian Modeling Techniques
Omid Sedehi, Antonina M. Kosikova, Costas Papadimitriou +1
Machine Learning (ML) has widely been used for modeling and predicting physical systems. These techniques offer high expressive power and good generalizability for interpolation wi…
Bayesian Structural Identification using Gaussian Process Discrepancy Models
Antonina M. Kosikova, Omid Sedehi, Costas Papadimitriou +1
Bayesian model updating based on Gaussian Process (GP) models has received attention in recent years, which incorporates kernel-based GPs to provide enhanced fidelity response pred…
Input-State-Parameter-Noise Identification and Virtual Sensing in Dynamical Systems: A Bayesian Expectation-Maximization (BEM) Perspective
Daniz Teymouri, Omid Sedehi, Lambros S. Katafygiotis +1
Structural identification and damage detection can be generalized as the simultaneous estimation of input forces, physical parameters, and dynamical states. Although Kalman-type fi…
Hierarchical Bayesian Uncertainty Quantification of Finite Element Models using Modal Statistical Information
Omid Sedehi, Costas Papadimitriou, Lambros S. Katafygiotis
This paper develops a Hierarchical Bayesian Modeling (HBM) framework for uncertainty quantification of Finite Element (FE) models based on modal information. This framework uses an…
On the Potential of Dynamic Substructuring Methods for Model Updating
Thomas Simpson, Vasilis Dertimanis, Costas Papadimitriou +1
While purely data-driven assessment is feasible for the first levels of the Structural Health Monitoring (SHM) process, namely damage detection and arguably damage localization, th…
Data-Driven Uncertainty Quantification and Propagation in Structural Dynamics through a Hierarchical Bayesian Framework
Omid Sedehi, Costas Papadimitriou, Lambros S. Katafygiotis
In the presence of modeling errors, the mainstream Bayesian methods seldom give a realistic account of uncertainties as they commonly underestimate the inherent variability of para…
Optimal sensing for fish school identification
Pascal Weber, Georgios Arampatzis, Guido Novati +3
Fish schooling implies an awareness of the swimmers for their companions. In flow mediated environments, in addition to visual cues, pressure and shear sensors on the fish body are…
Hierarchical Bayesian Operational Modal Analysis: Theory and Computations
Omid Sedehi, Lambros S. Katafygiotis, Costas Papadimitriou
This paper presents a hierarchical Bayesian modeling framework for the uncertainty quantification in modal identification of linear dynamical systems using multiple vibration data…
Optimal sensor placement for artificial swimmers
Siddhartha Verma, Costas Papadimitriou, Nora Luethen +2
Natural swimmers rely for their survival on sensors that gather information from the environment and guide their actions. The spatial organization of these sensors, such as the vis…
Data-driven prediction and origin identification of epidemics in population networks
Karen Larson, Clark Bowman, Zhizhong Chen +4
Effective intervention strategies for epidemics rely on the identification of their origin and on the robustness of the predictions made by network disease models. We introduce a B…
Experimental data over quantum mechanics simulations for inferring the repulsive exponent of the Lennard-Jones potential in Molecular Dynamics
Lina Kulakova, Georgios Arampatzis, Panagiotis Angelikopoulos +3
The Lennard-Jones (LJ) potential is a cornerstone of Molecular Dynamics (MD) simulations and among the most widely used computational kernels in science. The potential models atomi…