Improving Environmental Model Calibration and Prediction

Abstract

First, we have continued to develop tools for efficient global optimization of environmental models. Our algorithms are hybrid algorithms that combine evolutionary strategies for global search with derivative-free methods for local search in novel and efficient ways. Results thus far are promising and are pointing the way toward practical hybrid optimization tools for environmental models. Second, we are applying function approximation techniques to improve the efficiency of Monte Carlo Markov Chain simulations used in Bayesian optimization of environmental models. Very simple preliminary experiments have yielded 20-30% reduction in computational effort for well converged estimates of model parameter probability distributions.

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Document Details

Document Type
Technical Report
Publication Date
Jan 18, 2011
Accession Number
ADA544854

Entities

People

  • Jeffrey S. Baggett

Tags

Communities of Interest

  • C4I
  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Bayesian Networks
  • Computational Complexity
  • Computational Science
  • Data Science
  • Databases
  • Differential Equations
  • Evolutionary Algorithms
  • Information Science
  • Knowledge Management
  • Monte Carlo Method
  • Particle Swarm Optimization
  • Probability
  • Probability Distributions
  • Random Variables
  • Statistical Algorithms
  • Surveys

Readers

  • Computational Modeling and Simulation
  • Operations Research

Technology Areas

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