Scalable information inequalities for uncertainty quantification
نویسندگان
چکیده
منابع مشابه
Scalable Information Inequalities for Uncertainty Quantification
In this paper we demonstrate the only available scalable information bounds for quantities of interest of high dimensional probabilistic models. Scalability of inequalities allows us to (a) obtain uncertainty quantification bounds for quantities of interest in the large degree of freedom limit and/or at long time regimes; (b) assess the impact of large model perturbations as in nonlinear respon...
متن کاملIterative Methods for Scalable Uncertainty Quantification in Complex Networks
In this paper we address the problem of uncertainty management for robust design, and verification of large dynamic networks whose performance is affected by an equally large number of uncertain parameters. Many such networks (e.g. power, thermal and communication networks) are often composed of weakly interacting subnetworks. We propose intrusive and non-intrusive iterative schemes that exploi...
متن کاملDeveloping the Next Generation Scalable Exascale Uncertainty Quantification Methods
Predictive modeling of multiscale and multiphysics systems requires accurate data-driven characterization of the input uncertainties and understanding how they propagate across scales and alter the final solution. We will address three major current limitations in modeling stochastic systems: (1) Most of current uncertainty quantification methods cannot detect and handle discontinuity in the pa...
متن کاملInformation Theoretic Quantification of Diagnostic Uncertainty
Diagnostic test interpretation remains a challenge in clinical practice. Most physicians receive training in the use of Bayes' rule, which specifies how the sensitivity and specificity of a test for a given disease combine with the pre-test probability to quantify the change in disease probability incurred by a new test result. However, multiple studies demonstrate physicians' deficiencies in p...
متن کاملForward and Backward Uncertainty Quantification in Optimization
This contribution gathers some of the ingredients presented during the Iranian Operational Research community gathering in Babolsar in 2019.It is a collection of several previous publications on how to set up an uncertainty quantification (UQ) cascade with ingredients of growing computational complexity for both forward and reverse uncertainty propagation.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Computational Physics
سال: 2017
ISSN: 0021-9991
DOI: 10.1016/j.jcp.2017.02.020