Bayesian uncertainty quantification and information fusion in CALPHAD-based thermodynamic modeling
نویسندگان
چکیده
منابع مشابه
Forward 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 کاملSequential Designs Based on Bayesian Uncertainty Quantification in Sparse Representation Surrogate Modeling
A numerical method, called OBSM, was recently proposed which employs overcomplete basis functions to achieve sparse representations. While the method can handle non-stationary response without the need of inverting large covariance matrices, it lacks the capability to quantify uncertainty in predictions. We address this issue by proposing a Bayesian approach which first imposes a normal prior o...
متن کاملUncertainty Quantification in Bayesian In- version
Probabilistic thinking is of growing importance in many areas of mathematics. This paper highlights the beautiful mathematical framework, coupled with practical algorithms, which results from thinking probabilistically about inverse problems arising in partial differential equations. Many inverse problems in the physical sciences require the determination of an unknown field from a finite set o...
متن کاملQuantification, Optimization and Uncertainty Modeling in Information Security Risks: A Matrix-Based Approach
In this article, the authors present a quantitative model for estimating security risk exposure for a firm. The model includes a formulation for the optimization of controls as well as determining sensitivity of the exposure of assets to different threats. The model uses a series of matrices to organize the data as groups of assets, vulnerabilities, threats, and controls. The matrices are then ...
متن کاملBayesian Solution Uncertainty Quantification for Differential Equations
We explore probability modelling of discretization uncertainty for system states defined implicitly by ordinary or partial differential equations. Accounting for this uncertainty can avoid posterior under-coverage when likelihoods are constructed from a coarsely discretized approximation to system equations. A formalism is proposed for inferring a fixed but a priori unknown model trajectory thr...
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: Acta Materialia
سال: 2019
ISSN: 1359-6454
DOI: 10.1016/j.actamat.2018.11.007