Model Induction from Data: Towards the Next Generation of Computational Engines in Hydraulics and Hydrology
暫譯: 從數據中進行模型歸納:邁向下一代水力學與水文學計算引擎

Dibike, Y. B.

  • 出版商: CRC
  • 出版日期: 2017-10-02
  • 售價: $5,760
  • 貴賓價: 9.5$5,472
  • 語言: 英文
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 1138474797
  • ISBN-13: 9781138474796
  • 海外代購書籍(需單獨結帳)

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商品描述

There has been an explosive growth of methods in recent years for learning (or estimating dependency) from data, where data refers to known samples that are combinations of inputs and corresponding outputs of a given physical system. The main subject addressed in this thesis is model induction from data for the simulation of hydrodynamic processes in the aquatic environment. Firstly, some currently popular artificial neural network architectures are introduced, and it is then argued that these devices can be regarded as domain knowledge incapsulators by applying the method to the generation of wave equations from hydraulic data and showing how the equations of numerical-hydraulic models can, in their turn, be recaptured using artificial neural networks. The book also demonstrates how artificial neural networks can be used to generate numerical operators on non-structured grids for the simulation of hydrodynamic processes in two-dimensional flow systems and a methodology has been derived for developing generic hydrodynamic models using artificial neural network. The book also highlights one other model induction technique, namely that of support vector machine, as an emerging new method with a potential to provide more robust models.

商品描述(中文翻譯)

近年來,從數據中學習(或估計依賴性)的方法呈現爆炸性增長,其中數據指的是已知樣本,這些樣本是給定物理系統的輸入和相應輸出的組合。本論文主要探討從數據中進行模型歸納,以模擬水環境中的流體動力學過程。首先,介紹了一些當前流行的人工神經網絡架構,接著論證這些裝置可以被視為領域知識的封裝器,通過將該方法應用於從水力數據生成波動方程,並展示如何使用人工神經網絡重新捕捉數值水力模型的方程。本書還展示了如何使用人工神經網絡在非結構化網格上生成數值運算子,以模擬二維流動系統中的流體動力學過程,並衍生出一種使用人工神經網絡開發通用流體動力學模型的方法論。本書還強調了另一種模型歸納技術,即支持向量機,作為一種新興的方法,具有提供更穩健模型的潛力。

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