\(\Pi\)4U: a high performance computing framework for Bayesian uncertainty quantification of complex models

DOI10.1016/j.jcp.2014.12.006zbMath1352.65009OpenAlexW1985320029WikidataQ59760444 ScholiaQ59760444MaRDI QIDQ728966

Petros Koumoutsakos, P. E. Hadjidoukas, Costas Papadimitriou, Panagiotis Angelikopoulos

Publication date: 20 December 2016

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jcp.2014.12.006

zbMATH Keywords

reliabilityparallel computingdistributed computingBayesian inferenceuncertainty quantification

Mathematics Subject Classification ID

Related Items (11)

Bayesian analysis of rare events ⋮ A Bayesian framework to estimate fluid and material parameters in micro-swimmer models ⋮ A Riemannian stochastic representation for quantifying model uncertainties in molecular dynamics simulations ⋮ A hierarchical Bayesian framework for force field selection in molecular dynamics simulations ⋮ Data-driven selection and parameter estimation for DNA methylation mathematical models ⋮ Langevin Diffusion for Population Based Sampling with an Application in Bayesian Inference for Pharmacodynamics ⋮ Large-scale stochastic topology optimization using adaptive mesh refinement and coarsening through a two-level parallelization scheme ⋮ An iterative Bayesian filtering framework for fast and automated calibration of DEM models ⋮ Multilevel Control Variates for Uncertainty Quantification in Simulations of Cloud Cavitation ⋮ Pi4U ⋮ Korali: efficient and scalable software framework for Bayesian uncertainty quantification and stochastic optimization

Uses Software

• Gromacs
• CMA-ES
• DAKOTA
• ABC-SubSim
• NDL
• QUESO
• PSUADE
• TORC
• mystic

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