Algorithms for Model Calibration of Ground Water Simulators

Abstract

The PI and his students design, analyze, and implement novel algorithms for model calibration including method for noisy and ill-conditioned nonlinear least squares problems, reduced order models (such as POD and sparse interpolation), and methods based on Bayesian analysis which are part of uncertainty quantification. We also work on simulation methods such as flow in the vadose zone, non-Darcy flow models, linear and nonlinear solvers, pseudo-transient continuation, and preconditioning.

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Document Details

Document Type
Technical Report
Publication Date
Nov 20, 2014
Accession Number
ADA622181

Entities

People

  • Carl Timothy Kelley

Organizations

  • North Carolina State University

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Applied Mathematics
  • Bayesian Networks
  • Calibration
  • Chemistry
  • Computational Science
  • Engineering
  • Equations
  • Groundwater
  • Hong Kong
  • Inverse Problems
  • Mathematics
  • Monte Carlo Method
  • Simulations
  • Students
  • Water
  • Water Resources

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computational Fluid Dynamics (CFD)
  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms