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Polynomial-chaos-based conditional statistics for probabilistic learning with heterogeneous data applied to atomic collisions of helium on graphite substrate - MaRDI portal

Polynomial-chaos-based conditional statistics for probabilistic learning with heterogeneous data applied to atomic collisions of helium on graphite substrate (Q6198152)

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scientific article; zbMATH DE number 7807989

Language	Label	Description	Also known as
English	Polynomial-chaos-based conditional statistics for probabilistic learning with heterogeneous data applied to atomic collisions of helium on graphite substrate	scientific article; zbMATH DE number 7807989

Statements

scholarly article

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Polynomial-chaos-based conditional statistics for probabilistic learning with heterogeneous data applied to atomic collisions of helium on graphite substrate (English)

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Christian Soize

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Journal of Computational Physics

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publication date

21 February 2024

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zbMATH Keywords

polynomial chaos expansion

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statistical surrogate model

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probabilistic learning

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heterogeneous data

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uncertainty quantification

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atomic collisions

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MaRDI profile type

MaRDI publication profile

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full work available at URL

https://doi.org/10.1016/j.jcp.2023.112582

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Reduced chaos decomposition with random coefficients of vector-valued random variables and random fields

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The orthogonal development of non-linear functionals in series of Fourier-Hermite functionals

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Polynomial Chaos in Stochastic Finite Elements

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The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations

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Physical Systems with Random Uncertainties: Chaos Representations with Arbitrary Probability Measure

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Generalized polynomial chaos and random oscillators

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Polynomial Chaos Expansion of Random Coefficients and the Solution of Stochastic Partial Differential Equations in the Tensor Train Format

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Basis adaptation in homogeneous chaos spaces

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Compressed Principal Component Analysis of Non-Gaussian Vectors

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Stochastic convergence acceleration through basis enrichment of polynomial chaos expansions

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Convergence acceleration of polynomial chaos solutions via sequence transformation

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Dimensionality reduction and polynomial chaos acceleration of Bayesian inference in inverse problems

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Polynomial Chaos Expansion of a Multimodal Random Vector

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Numerical Challenges in the Use of Polynomial Chaos Representations for Stochastic Processes

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An adaptive multi-element generalized polynomial chaos method for stochastic differential equations

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Multi-Element Generalized Polynomial Chaos for Arbitrary Probability Measures

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Sparse polynomial chaos expansions and adaptive stochastic finite elements using a regression approach

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Polynomial chaos representation of spatio-temporal random fields from experimental measurements

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On the convergence of generalized polynomial chaos expansions

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Solving elliptic boundary value problems with uncertain coefficients by the finite element method: the stochastic formulation

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Galerkin methods for linear and nonlinear elliptic stochastic partial differential equations

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A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data

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A generalized spectral decomposition technique to solve a class of linear stochastic partial differential equations

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Generalized spectral decomposition method for solving stochastic finite element equations: invariant subspace problem and dedicated algorithms

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Generalized spectral decomposition for stochastic nonlinear problems

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Proper generalized decompositions and separated representations for the numerical solution of high dimensional stochastic problems

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Spectral Methods for Uncertainty Quantification

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Uncertainty Quantification and Polynomial Chaos Techniques in Computational Fluid Dynamics

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Numerical solution of spectral stochastic finite element systems

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Propagation of probabilistic uncertainty in complex physical systems using a stochastic finite element approach

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Ingredients for a general purpose stochastic finite elements implementation

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Iterative solution of systems of linear equations arising in the context of stochastic finite elements

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Solution of stochastic partial differential equations using Galerkin finite element techniques

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Finite elements for elliptic problems with stochastic coefficients

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Stochastic finite element: a non intrusive approach by regression

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A multiscale stochastic finite element method on elliptic problems involving uncertainties

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Stochastic finite elements: Computational approaches to stochastic partial differential equations

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Adaptive sparse polynomial chaos expansion based on least angle regression

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Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark

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Statistical and computational inverse problems.

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Bayesian Calibration of Computer Models

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Inverse Problem Theory and Methods for Model Parameter Estimation

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Inverse problems: A Bayesian perspective

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On the Brittleness of Bayesian Inference

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Sampling of Bayesian posteriors with a non-Gaussian probabilistic learning on manifolds from a small dataset

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A scalable framework for the solution of stochastic inverse problems using a sparse grid collocation approach

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An efficient Bayesian inference approach to inverse problems based on an adaptive sparse grid collocation method

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Fast Algorithms for Bayesian Uncertainty Quantification in Large-Scale Linear Inverse Problems Based on Low-Rank Partial Hessian Approximations

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Bayesian inference with optimal maps

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Karhunen-Loève expansion revisited for vector-valued random fields: scaling, errors and optimal basis.

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Bayesian adaptation of chaos representations using variational inference and sampling on geodesics

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An approximate empirical Bayesian method for large-scale linear-Gaussian inverse problems

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Adaptive method for indirect identification of the statistical properties of random fields in a Bayesian framework

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Maximum likelihood estimation of stochastic chaos representations from experimental data

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Asymptotic Sampling Distribution for Polynomial Chaos Representation from Data: A Maximum Entropy and Fisher Information Approach

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Identification of Bayesian posteriors for coefficients of chaos expansions

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Identification of high-dimension polynomial chaos expansions with random coefficients for non-Gaussian tensor-valued random fields using partial and limited experimental data

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A computational inverse method for identification of non-Gaussian random fields using the Bayesian approach in very high dimension

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Identification of Polynomial Chaos Representations in High Dimension from a Set of Realizations

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Parameter estimation using polynomial chaos and maximum likelihood

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Spectral likelihood expansions for Bayesian inference

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Coordinate transformation and polynomial chaos for the Bayesian inference of a Gaussian process with parametrized prior covariance function

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Bayesian sparse polynomial chaos expansion for global sensitivity analysis

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Probabilistic model identification of uncertainties in computational models for dynamical systems and experimental validation

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Identification and sampling of Bayesian posteriors of high-dimensional symmetric positive-definite matrices for data-driven updating of computational models

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Multi-fidelity Gaussian process regression for prediction of random fields

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Adaptive Bayesian SLOPE: Model Selection With Incomplete Data

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Kriging metamodeling in simulation: a review

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Active Subspace Methods in Theory and Practice: Applications to Kriging Surfaces

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A new surrogate modeling technique combining Kriging and polynomial chaos expansions - application to uncertainty analysis in computational dosimetry

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Regression and Kriging metamodels with their experimental designs in simulation: a review

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Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold

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Surrogate modeling of high-dimensional problems via data-driven polynomial chaos expansions and sparse partial least square

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Kernel methods in machine learning

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Learning ``best'' kernels from data in Gaussian process regression. With application to aerodynamics

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An Introduction to Statistical Learning

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Statistical learning and selective inference

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Projection-based model reduction: formulations for physics-based machine learning

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Data-driven probability concentration and sampling on manifold

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Nested polynomial trends for the improvement of Gaussian process-based predictors

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Polynomial chaos representation of databases on manifolds

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Data-driven kernel representations for sampling with an unknown block dependence structure under correlation constraints

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Entropy-based closure for probabilistic learning on manifolds

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Probabilistic learning on manifolds (PLoM) with partition

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Physics-Informed Probabilistic Learning of Linear Embeddings of Nonlinear Dynamics with Guaranteed Stability

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Probabilistic learning on manifolds constrained by nonlinear partial differential equations for small datasets

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Probabilistic learning inference of boundary value problem with uncertainties based on Kullback-Leibler divergence under implicit constraints

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The Nested Dirichlet Process

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Uncertainty quantification. An accelerated course with advanced applications in computational engineering

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Computational Aspects for Constructing Realizations of Polynomial Chaos in High Dimension

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Algorithm AS 136: A K-Means Clustering Algorithm

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Hierarchical agglomerative clustering procedure

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Mean shift-based clustering

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Variable Selection for Clustering with Gaussian Mixture Models

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Identifiers

Mathematics Subject Classification ID

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zbMATH DE Number

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10.1016/J.JCP.2023.112582

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Sitelinks

Mathematics(1 entry)

mardi Publication:6198152

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