Data-driven uncertainty quantification for predictive flow and transport modeling using support vector machines

DOI10.1007/s10596-018-9762-4zbMath1421.76224OpenAlexW2887059130WikidataQ129478963 ScholiaQ129478963MaRDI QIDQ2321952

Jiachuan He, Steven Mattis, Troy Butler, Clint N. Dawson

Publication date: 27 August 2019

Published in: Computational Geosciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10596-018-9762-4

zbMATH Keywords

parameter estimationhydraulic conductivitymeasure theorysupport vector regressionstochastic inverse problems

Mathematics Subject Classification ID

Bayesian inference (62F15) Flows in porous media; filtration; seepage (76S05) Stochastic analysis applied to problems in fluid mechanics (76M35) Finite element, Rayleigh-Ritz and Galerkin methods for initial value and initial-boundary value problems involving PDEs (65M60)

Related Items (5)

The Inverse Heat Transfer Problem of Malan Loess Based on Machine Learning with Finite Element Solver as the Trainer ⋮ Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator: concept for bias correction, assessment of surrogate reliability and its application to the carbon dioxide benchmark ⋮ Preconditioning Markov chain Monte Carlo method for geomechanical subsidence using multiscale method and machine learning technique ⋮ Physics-informed machine learning models for predicting the progress of reactive-mixing ⋮ Optimal feature selection for SAR image classification using biogeography-based optimization (BBO), artificial bee colony (ABC) and support vector machine (SVM): a combined approach of optimization and machine learning

Uses Software

• FEniCS
• LIBSVM
• MLMSRBF

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Automated solution of differential equations by the finite element method. The FEniCS book
• Quantifying uncertainty in material damage from vibrational data
• Stochastic spectral methods for efficient Bayesian solution of inverse problems
• An efficient, high-order perturbation approach for flow in random porous media via Karhunen-Loève and polynomial expansions.
• On the Brittleness of Bayesian Inference
• A Stochastic Radial Basis Function Method for the Global Optimization of Expensive Functions
• A Measure-Theoretic Computational Method for Inverse Sensitivity Problems III: Multiple Quantities of Interest
• Spectral Methods for Uncertainty Quantification
• Combining Push-Forward Measures and Bayes' Rule to Construct Consistent Solutions to Stochastic Inverse Problems
• The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations
• A Measure-Theoretic Interpretation of Sample Based Numerical Integration with Applications to Inverse and Prediction Problems under Uncertainty
• Gene selection for cancer classification using support vector machines

This page was built for publication: Data-driven uncertainty quantification for predictive flow and transport modeling using support vector machines