Exploiting Task-Based Parallelism in Bayesian Uncertainty Quantification. - Panagiotis E. Hadjidoukas; Panagiotis Angelikopoulos; Lina Kulakova; Costas Papadimitriou; Petros Koumoutsakos (Euro-Par, 2015)

Π4U: A high performance computing framework for Bayesian uncertainty quantification of complex models. - Panagiotis E. Hadjidoukas; Panagiotis Angelikopoulos; Costas Papadimitriou; Petros Koumoutsakos (J. Comput. Phys., 2015)

Approximate Bayesian Computation for Granular and Molecular Dynamics Simulations. - Lina Kulakova; Panagiotis Angelikopoulos; Panagiotis E. Hadjidoukas; Costas Papadimitriou; Petros Koumoutsakos (PASC, 2016)

Model-reduction techniques for reliability-based design problems of complex structural systems. - Hector A. Jensen; Álvaro Muñoz; Costas Papadimitriou; Eduardo Millas (Reliab. Eng. Syst. Saf., 2016)

Implementation of an adaptive meta-model for Bayesian finite element model updating in time domain. - Hector A. Jensen; C. Esse; V. Araya; Costas Papadimitriou (Reliab. Eng. Syst. Saf., 2017)

On the Potenital of Dynamic Substructuring Methods for Model Updating. - Thomas Simpson; Vasilis K. Dertimanis; Costas Papadimitriou; Eleni N. Chatzi (CoRR, 2020)

Bayesian Model-Updating Using Features of Modal Data: Application to the Metsovo Bridge. - Costas Argyris; Costas Papadimitriou; Panagiotis Panetsos; Panos Tsopelas (J. Sens. Actuator Networks, 2020)

Accounting for Modeling Errors and Inherent Structural Variability through a Hierarchical Bayesian Model Updating Approach: An Overview. - Mingming Song; Iman Behmanesh; Babak Moaveni; Costas Papadimitriou (Sensors, 2020)

Optimal Sensor Placement for Reliable Virtual Sensing Using Modal Expansion and Information Theory. - Tulay Ercan; Costas Papadimitriou (Sensors, 2021)

On the Integration of Physics-Based Machine Learning with Hierarchical Bayesian Modeling Techniques. - Omid Sedehi; Antonina M. Kosikova; Costas Papadimitriou; Lambros S. Katafygiotis (CoRR, 2023)

Monitoring-Supported Value Generation for Managing Structures and Infrastructure Systems. - Antonios Kamariotis; Eleni N. Chatzi; Daniel Straub; Nikolaos Dervilis; Kai Goebel; Aidan J. Hughes; Geert Lombaert; Costas Papadimitriou; Konstantinos G. Papakonstantinou; Matteo Pozzi; Michael Todd; Keith Worden (CoRR, 2024)

Physics-informed neural networks for parameter learning of wildfire spreading. - Konstantinos Vogiatzoglou; Costas Papadimitriou; Vasilis Bontozoglou; Konstantinos Ampountolas (CoRR, 2024)

Navigating Uncertainties in Machine Learning for Structural Dynamics: A Comprehensive Review of Probabilistic and Non-Probabilistic Approaches in Forward and Inverse Problems. - Wang-Ji Yan; Lin-Feng Mei; Jiang Mo; Costas Papadimitriou; Ka-Veng Yuen; Michael Beer (CoRR, 2024)

An interpretable wildfire spreading model for real-time predictions. - Konstantinos Vogiatzoglou; Costas Papadimitriou; Konstantinos Ampountolas; Michail Chatzimanolakis; Petros Koumoutsakos; Vasilis Bontozoglou (J. Comput. Sci., 2024)

Learning non-stationary model of prediction errors with hierarchical Bayesian modeling. - Menghao Ping; Wang-Ji Yan; Xinyu Jia; Costas Papadimitriou; Ka-Veng Yuen (Reliab. Eng. Syst. Saf., 2025)