Machine learning subsurface flow equations from data

DOI10.1007/s10596-019-09847-2zbMath1425.86003OpenAlexW2959482009MaRDI QIDQ2009819

Haibin Chang, Dongxiao Zhang

Publication date: 2 December 2019

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

Full work available at URL: https://doi.org/10.1007/s10596-019-09847-2

zbMATH Keywords

noisy datamachine learningLASSOgoverning equationsdata-driven discovery

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Hydrology, hydrography, oceanography (86A05)

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Uses Software

• ElemStatLearn

Cites Work

• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• Discovering governing equations from data by sparse identification of nonlinear dynamical systems
• The Adaptive Lasso and Its Oracle Properties
• A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
• Numerical differentiation of noisy, nonsmooth data
• Least angle and \(\ell _{1}\) penalized regression: a review
• Numerical differentiation with noisy signal
• On the recovery of multiple flow parameters from transient head data
• Least angle regression. (With discussion)
• The Lasso problem and uniqueness
• Identification of physical processes via combined data-driven and data-assimilation methods
• Piecewise linear regularized solution paths
• Numerical Differentiation of Approximated Functions with Limited Order-of-Accuracy Deterioration
• Automated reverse engineering of nonlinear dynamical systems
• Model selection for dynamical systems via sparse regression and information criteria
• Learning partial differential equations via data discovery and sparse optimization
• Numerical Differentiation and Regularization
• Unnamed Item