Listar por autor "Angelikopoulos, P."
Mostrando ítems 1-8 de 8
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Bayesian Hierarchical Models for Uncertainty Quantification in Structural Dynamics
Ballesteros, G. C.; Angelikopoulos, P.; Papadimitriou, C.; Koumoutsakos, P. (2014)The Bayesian framework for hierarchical modeling is applied to quantify uncertainties, arising mainly due to manufacturing variability, for a group of identical structural components. Parameterized Gaussian models of ... -
Bayesian uncertainty quantification and propagation for discrete element simulations of granular materials
Hadjidoukas, P. E.; Angelikopoulos, P.; Rossinelli, D.; Alexeev, D.; Papadimitriou, C.; Koumoutsakos, P. (2014)Predictions in the behavior of granular materials using Discrete Element Methods (DEM) hinge on the employed interaction potentials. Here we introduce a data driven, Bayesian framework to quantify DEM predictions. Our ... -
Bayesian uncertainty quantification and propagation in molecular dynamics simulations
Angelikopoulos, P.; Papadimitriou, C.; Koumoutsakos, P. (2012)A comprehensive Bayesian probabilistic framework is developed for quantifying and calibrating the uncertainties in the parameters of the models (e.g. force-field potentials) involved in molecular dynamics (MD) simulations ... -
Bayesian uncertainty quantification and propagation in molecular dynamics simulations: A high performance computing framework
Angelikopoulos, P.; Papadimitriou, C.; Koumoutsakos, P. (2012)We present a Bayesian probabilistic framework for quantifying and propagating the uncertainties in the parameters of force fields employed in molecular dynamics (MD) simulations. We propose a highly parallel implementation ... -
Data Driven, Predictive Molecular Dynamics for Nanoscale Flow Simulations under Uncertainty
Angelikopoulos, P.; Papadimitriou, C.; Koumoutsakos, P. (2013)For over five decades, molecular dynamics (MD) simulations have helped to elucidate critical mechanisms in a broad range of physiological systems and technological innovations. MD simulations are synergetic with experiments, ... -
Efficient techniques for bayesian inverse modeling of large-order computational models
Papadimitriou, C.; Angelikopoulos, P.; Koumoutsakos, P.; Papadioti, D. C. (2013)Bayesian tools for inverse modeling are based on asymptotic approximations and Stochastic Simulation Algorithms (SSA). Such tools require a number of moderate to large number of system re-analyses. For large-order numerical ... -
Pi 4U: A high performance computing framework for Bayesian uncertainty quantification of complex models
Hadjidoukas, P. E.; Angelikopoulos, P.; Papadimitriou, C.; Koumoutsakos, P. (2015)We present Pi 4U,(1) an extensible framework, for non-intrusive Bayesian Uncertainty Quantification and Propagation (UQ+P) of complex and computationally demanding physical models, that can exploit massively parallel ... -
X-TMCMC: Adaptive kriging for Bayesian inverse modeling
Angelikopoulos, P.; Papadimitriou, C.; Koumoutsakos, P. (2015)The Bayesian inference of models associated with large-scale simulations is prohibitively expensive even for massively parallel architectures. We demonstrate that we can drastically reduce this cost by combining adaptive ...