Pi4U: A high performance computing framework for Bayesian uncertainty quantification of complex models. We present Pi4U, 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 computer architectures. The framework incorporates Laplace asymptotic approximations as well as stochastic algorithms, along with distributed numerical differentiation and task-based parallelism for heterogeneous clusters. Sampling is based on the Transitional Markov Chain Monte Carlo (TMCMC) algorithm and its variants. The optimization tasks associated with the asymptotic approximations are treated via the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). A modified subset simulation method is used for posterior reliability measurements of rare events. The framework accommodates scheduling of multiple physical model evaluations based on an adaptive load balancing library and shows excellent scalability. In addition to the software framework, we also provide guidelines as to the applicability and efficiency of Bayesian tools when applied to computationally demanding physical models. Theoretical and computational developments are demonstrated with applications drawn from molecular dynamics, structural dynamics and granular flow.

References in zbMATH (referenced in 9 articles , 1 standard article )

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  1. Martin, Sergio M.; Wälchli, Daniel; Arampatzis, Georgios; Economides, Athena E.; Karnakov, Petr; Koumoutsakos, Petros: Korali: efficient and scalable software framework for Bayesian uncertainty quantification and stochastic optimization (2022)
  2. Larson, Karen; Olson, Sarah D.; Matzavinos, Anastasios: A Bayesian framework to estimate fluid and material parameters in micro-swimmer models (2021)
  3. Baiges, Joan; Martínez-Frutos, Jesús; Herrero-Pérez, David; Otero, Fermin; Ferrer, Alex: Large-scale stochastic topology optimization using adaptive mesh refinement and coarsening through a two-level parallelization scheme (2019)
  4. Cheng, Hongyang; Shuku, Takayuki; Thoeni, Klaus; Tempone, Pamela; Luding, Stefan; Magnanimo, Vanessa: An iterative Bayesian filtering framework for fast and automated calibration of DEM models (2019)
  5. Larson, Karen; Zagkos, Loukas; Mc Auley, Mark; Roberts, Jason; Kavallaris, Nikos I.; Matzavinos, Anastasios: Data-driven selection and parameter estimation for DNA methylation mathematical models (2019)
  6. Arampatzis, Georgios; Wälchli, Daniel; Angelikopoulos, Panagiotis; Wu, Stephen; Hadjidoukas, Panagiotis; Koumoutsakos, Petros: Langevin diffusion for population based sampling with an application in Bayesian inference for pharmacodynamics (2018)
  7. Šukys, Jonas; Rasthofer, Ursula; Wermelinger, Fabian; Hadjidoukas, Panagiotis; Koumoutsakos, Petros: Multilevel control variates for uncertainty quantification in simulations of cloud cavitation (2018)
  8. Straub, Daniel; Papaioannou, Iason; Betz, Wolfgang: Bayesian analysis of rare events (2016)
  9. Hadjidoukas, P. E.; Angelikopoulos, P.; Papadimitriou, C.; Koumoutsakos, P.: (\Pi)4U: a high performance computing framework for Bayesian uncertainty quantification of complex models (2015)