Approximate Solutions of Lagrange Multipliers for Information-Theoretic Random Field Models
DOI10.1137/14099574XzbMath1323.74004OpenAlexW2183080157MaRDI QIDQ2945169
Publication date: 9 September 2015
Published in: SIAM/ASA Journal on Uncertainty Quantification (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1137/14099574x
elasticityrandom fieldmaximum entropy principleuncertainty quantificationinformation-theoretic stochastic modelstochastic elliptic boundary value problem
Probabilistic models, generic numerical methods in probability and statistics (65C20) Monte Carlo methods (65C05) Classical linear elasticity (74B05) Effective constitutive equations in solid mechanics (74Q15) Random materials and composite materials (74A40) Stochastic and other probabilistic methods applied to problems in solid mechanics (74S60)
Related Items (3)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A Mathematical Theory of Communication
- On the statistical dependence for the components of random elasticity tensors exhibiting material symmetry properties
- The closest elastic tensor of arbitrary symmetry to an elasticity tensor of lower symmetry
- Weak convergence and optimal scaling of random walk Metropolis algorithms
- Non-Gaussian positive-definite matrix-valued random fields for elliptic stochastic partial differential operators
- The orthogonal development of non-linear functionals in series of Fourier-Hermite functionals
- Stochastic Model and Generator for Random Fields with Symmetry Properties: Application to the Mesoscopic Modeling of Elastic Random Media
- Information Theory and Statistical Mechanics
- Construction of probability distributions in high dimension using the maximum entropy principle: Applications to stochastic processes, random fields and random matrices
- Spectral Methods for Uncertainty Quantification
- Fourth-rank tensors of the thirty-two crystal classes: multiplication tables
- Probability Theory
- Itô SDE--based Generator for a Class of Non-Gaussian Vector-valued Random Fields in Uncertainty Quantification
- Equation of State Calculations by Fast Computing Machines
- Stochastic Models of Uncertainties in Computational Mechanics
- Numerical Methods for Second‐Order Stochastic Differential Equations
- Random matrices and information theory
- Monte Carlo sampling methods using Markov chains and their applications
This page was built for publication: Approximate Solutions of Lagrange Multipliers for Information-Theoretic Random Field Models