An Adaptive Metropolis-Hasting Sampling Algorithm for Reservoir Uncertainty Quantification in Bayesian Inference

2015 ◽  
Author(s):  
Asaad Abdollahzadeh ◽  
Mike Christie ◽  
David Corne
Keyword(s):  
Bayesian Inference ◽  
Uncertainty Quantification ◽  
Sampling Algorithm

Multiscale Variability and Uncertainty Quantification Based on a Generalized Multiscale Markov Model

2010 ◽  
Author(s):  
Yan Wang
Keyword(s):  
Probability Theory ◽  
Bayesian Inference ◽  
Markov Model ◽  
Uncertainty Quantification ◽  
System Analysis ◽  
Scale Validation ◽  
Uncertainty Propagation ◽  
Bayes Rule ◽  
Imprecise Probability ◽  
Modal Extension

Variability is inherent randomness in systems, whereas uncertainty is due to lack of knowledge. In this paper, a generalized multiscale Markov (GMM) model is proposed to quantify variability and uncertainty simultaneously in multiscale system analysis. The GMM model is based on a new imprecise probability theory that has the form of generalized interval, which is a Kaucher or modal extension of classical set-based intervals to represent uncertainties. The properties of the new definitions of independence and Bayesian inference are studied. Based on a new Bayes’ rule with generalized intervals, three cross-scale validation approaches that incorporate variability and uncertainty propagation are also developed.

SPUX - a Scalable Package for Bayesian Uncertainty Quantification

2020 ◽  
Author(s):  
Jonas Sukys ◽  
Marco Bacci
Keyword(s):  
Bayesian Inference ◽  
Uncertainty Quantification ◽  
High Performance ◽  
Performance Metrics ◽  
Expert Knowledge ◽  
Simple Random Walk ◽  
Scientific Workflow ◽  
Model Parameters ◽  
Parameter Uncertainties ◽  
Computational Performance

<div> <div>SPUX (Scalable Package for Uncertainty Quantification in "X") is a modular framework for Bayesian inference and uncertainty quantification. The SPUX framework aims at harnessing high performance scientific computing to tackle complex aquatic dynamical systems rich in intrinsic uncertainties,</div> <div>such as ecological ecosystems, hydrological catchments, lake dynamics, subsurface flows, urban floods, etc. The challenging task of quantifying input, output and/or parameter uncertainties in such stochastic models is tackled using Bayesian inference techniques, where numerical sampling and filtering algorithms assimilate prior expert knowledge and available experimental data. The SPUX framework greatly simplifies uncertainty quantification for realistic computationally costly models and provides an accessible, modular, portable, scalable, interpretable and reproducible scientific workflow. To achieve this, SPUX can be coupled to any serial or parallel model written in any programming language (e.g. Python, R, C/C++, Fortran, Java), can be installed either on a laptop or on a parallel cluster, and has built-in support for automatic reports, including algorithmic and computational performance metrics. I will present key SPUX concepts using a simple random walk example, and showcase recent realistic applications for catchment and lake models. In particular, uncertainties in model parameters, meteorological inputs, and data observation processes are inferred by assimilating available in-situ and remotely sensed datasets.</div> </div>

Uncertainty Quantification in MD Simulations. Part II: Bayesian Inference of Force-Field Parameters

2012 ◽  
Vol 10 (4) ◽  
pp. 1460-1492 ◽  
Author(s):  
F. Rizzi ◽  
H. N. Najm ◽  
B. J. Debusschere ◽  
K. Sargsyan ◽  
M. Salloum ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Force Field ◽  
Uncertainty Quantification ◽  
Md Simulations ◽  
Force Field Parameters

scholarly journals Data-Informed Decomposition for Localized Uncertainty Quantification of Dynamical Systems

2020 ◽  
Author(s):  
Waad Subber ◽  
Sayan Ghosh ◽  
Piyush Pandita ◽  
Yiming Zhang ◽  
Liping Wang
Keyword(s):  
Bayesian Inference ◽  
Dynamical Systems ◽  
Uncertainty Quantification ◽  
Computational Cost ◽  
Three Dimensional ◽  
Region Of Interest ◽  
System Response ◽  
User Interest ◽  
Operation Conditions ◽  
Random Material Properties

Industrial dynamical systems often exhibit multi-scale response due to material heterogeneities, operation conditions and complex environmental loadings. In such problems, it is the case that the smallest length-scale of the systems dynamics controls the numerical resolution required to effectively resolve the embedded physics. In practice however, high numerical resolutions is only required in a confined region of the system where fast dynamics or localized material variability are exhibited, whereas a coarser discretization can be sufficient in the rest majority of the system. To this end, a unified computational scheme with uniform spatio-temporal resolutions for uncertainty quantification can be very computationally demanding. Partitioning the complex dynamical system into smaller easier-to-solve problems based of the localized dynamics and material variability can reduce the overall computational cost. However, identifying the region of interest for high-resolution and intensive uncertainty quantification can be a problem dependent. The region of interest can be specified based on the localization features of the solution, user interest, and correlation length of the random material properties. For problems where a region of interest is not evident, Bayesian inference can provide a feasible solution. In this work, we employ a Bayesian framework to update our prior knowledge on the localized region of interest using measurements and system response. To address the computational cost of the Bayesian inference, we construct a Gaussian process surrogate for the forward model. Once, the localized region of interest is identified, we use polynomial chaos expansion to propagate the localization uncertainty. We demonstrate our framework through numerical experiments on a three-dimensional elastodynamic problem

scholarly journals A Bayesian graph convolutional network for reliable prediction of molecular properties with uncertainty quantification

2019 ◽  
Vol 10 (36) ◽  
pp. 8438-8446 ◽  
Author(s):  
Seongok Ryu ◽  
Yongchan Kwon ◽  
Woo Youn Kim
Keyword(s):  
Neural Networks ◽  
Bayesian Inference ◽  
Uncertainty Analysis ◽  
Uncertainty Quantification ◽  
Deep Neural Networks ◽  
Molecular Properties ◽  
Reliable Prediction ◽  
Convolutional Network ◽  
Chemical Fields

Deep neural networks have been increasingly used in various chemical fields. Here, we show that Bayesian inference enables more reliable prediction with quantitative uncertainty analysis.

scholarly journals Bayeslands: A Bayesian inference approach for parameter uncertainty quantification in Badlands

2019 ◽  
Vol 131 ◽  
pp. 89-101 ◽  
Author(s):  
Rohitash Chandra ◽  
Danial Azam ◽  
R. Dietmar Müller ◽  
Tristan Salles ◽  
Sally Cripps
Keyword(s):  
Bayesian Inference ◽  
Uncertainty Quantification ◽  
Parameter Uncertainty

scholarly journals Uncertainty quantification for polymer micromilling force models using Bayesian inference

2020 ◽  
Vol 48 ◽  
pp. 611-618
Author(s):  
Shivang Shekhar ◽  
Kadri Bugra Ozutemiz ◽  
Recep Onler ◽  
Sudhanshu Nahata ◽  
O. Burak Ozdoganlar
Keyword(s):  
Bayesian Inference ◽  
Uncertainty Quantification
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