Using Neural Network parameterizations of the Nonlinear Energy Transfer for Application in Wave Models

Abstract

Considering the crucial importance of nonlinear interaction Snl for the development of third generation wave models the long-term goal of this work is to improve accuracy of calculating nonlinear interaction Snl in wind wave models, and hence improving wave prediction in general.

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Document Details

Document Type
Technical Report
Publication Date
Sep 30, 2001
Accession Number
ADA626714

Entities

People

  • Dmitry Chalikov
  • Hendrik L. Tolman
  • Vladimir M. Krasnopolsky

Organizations

  • National Oceanic and Atmospheric Administration

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Computing-Related Activities
  • Data Science
  • Data Sets
  • Deep Water
  • Demographic Cohorts
  • Energy
  • Energy Transfer
  • Errors
  • Feasibility Studies
  • Frequency
  • Information Operations
  • Neural Networks
  • Spectra
  • Training
  • Two Dimensional

Fields of Study

  • Physics

Readers

  • Coastal Oceanography
  • Computational Modeling and Simulation
  • Wave Propagation and Nonlinear Chaotic Dynamics.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks