Deep Importance Sampling Using Tensor Trains with Application to a Priori and a Posteriori Rare Events (Q6189161)

scientific article; zbMATH DE number 7801539

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English	Deep Importance Sampling Using Tensor Trains with Application to a Priori and a Posteriori Rare Events	scientific article; zbMATH DE number 7801539

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instance of

scholarly article

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title

Deep Importance Sampling Using Tensor Trains with Application to a Priori and a Posteriori Rare Events (English)

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SIAM Journal on Scientific Computing

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publication date

8 February 2024

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full work available at URL

https://arxiv.org/abs/2209.01941

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zbMATH Keywords

rare events

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Bayesian inference

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inverse problems

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tensor train

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transport maps

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MaRDI profile type

MaRDI publication profile

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cites work

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

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Spectral Tensor-Train Decomposition

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An efficient algorithm for rare-event probability estimation, combinatorial optimization, and counting

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Model Reduction for Large-Scale Systems with High-Dimensional Parametric Input Space

0 references

Sparse-grid, reduced-basis Bayesian inversion: nonaffine-parametric nonlinear equations

0 references

Parallel computation of flow in heterogeneous media modelled by mixed finite elements

0 references

Nonlinear Reduced Models for State and Parameter Estimation

0 references

Convergence rates of best \(N\)-term Galerkin approximations for a class of elliptic SPDEs

0 references

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

0 references

Deep composition of tensor-trains using squared inverse Rosenblatt transports

0 references

Scalable conditional deep inverse Rosenblatt transports using tensor trains and gradient-based dimension reduction

0 references

Likelihood-informed dimension reduction for nonlinear inverse problems

0 references

Data-driven model reduction for the Bayesian solution of inverse problems

0 references

Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction

0 references

A unified performance analysis of likelihood-informed subspace methods

0 references

Sequential Monte Carlo Samplers

0 references

Approximation and sampling of multivariate probability distributions in the tensor train decomposition

0 references

Alternating Minimal Energy Methods for Linear Systems in Higher Dimensions

0 references

Convergence of adaptive mixtures of importance sampling schemes

0 references

Low-rank tensor reconstruction of concentrated densities with application to Bayesian inversion

0 references

Sampling-free Bayesian inversion with adaptive hierarchical tensor representations

0 references

A Multilevel Monte Carlo Method for Computing Failure Probabilities

0 references

Methods for approximating integrals in statistics with special emphasis on Bayesian integration problems

0 references

Non‐linear model reduction for uncertainty quantification in large‐scale inverse problems

0 references

Simulating normalizing constants: From importance sampling to bridge sampling to path sampling

0 references

A continuous analogue of the tensor-train decomposition

0 references

Analysis of tensor approximation schemes for continuous functions

0 references

Tensor Spaces and Numerical Tensor Calculus

0 references

Q3740832

0 references

Parameter and State Model Reduction for Large-Scale Statistical Inverse Problems

0 references

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

0 references

Bayesian inference with optimal maps

0 references

TT-cross approximation for multidimensional arrays

0 references

Tensor-Train Decomposition

0 references

Transport Map Accelerated Markov Chain Monte Carlo

0 references

Multifidelity importance sampling

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Multifidelity Preconditioning of the Cross-Entropy Method for Rare Event Simulation and Failure Probability Estimation

0 references

Rank Bounds for Approximating Gaussian Densities in the Tensor-Train Format

0 references

Remarks on a Multivariate Transformation

0 references

Quasi-Monte Carlo and Multilevel Monte Carlo Methods for Computing Posterior Expectations in Elliptic Inverse Problems

0 references

Sparse deterministic approximation of Bayesian inverse problems

0 references

Q4614105

0 references

Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification

0 references

Cross-Entropy-Based Importance Sampling with Failure-Informed Dimension Reduction for Rare Event Simulation

0 references

Multilevel Sequential Importance Sampling for Rare Event Estimation

0 references

Error Analysis for Probabilities of Rare Events with Approximate Models

0 references

Coupling the reduced-order model and the generative model for an importance sampling estimator

0 references

The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations

0 references

Adaptive multi-fidelity polynomial chaos approach to Bayesian inference in inverse problems

0 references

An Adaptive Surrogate Modeling Based on Deep Neural Networks for Large-Scale Bayesian Inverse Problems

0 references

Certified dimension reduction in nonlinear Bayesian inverse problems

0 references

Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

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Mathematics(1 entry)

mardi Publication:6189161