Multi-working-condition one-dimensional wave equation solving method based on neural network

A wave equation and neural network technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve the time-consuming and labor-intensive problems of one-dimensional wave equations

Active Publication Date: 2021-10-01
HARBIN INST OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] The purpose of the present invention is to solve the time-consuming and labor-intensive problem of the neural network in solving the one-dimensional wave equati

Method used

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  • Multi-working-condition one-dimensional wave equation solving method based on neural network
  • Multi-working-condition one-dimensional wave equation solving method based on neural network
  • Multi-working-condition one-dimensional wave equation solving method based on neural network

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specific Embodiment approach 1

[0052] Specific implementation mode 1: In this implementation mode, the multi-working-condition one-dimensional wave equation solution method based on neural network is implemented according to the following steps:

[0053] Step 1: Establish the governing equation, the one-dimensional seismic wave equation in isotropic media is as follows:

[0054]

[0055] Among them, V represents the wave velocity, and u represents the displacement of the particle under the (x, t) coordinates;

[0056] Step 2. Determine the solution domain and the number of residual points:

[0057] Set the solution domain of x to [0,1], the solution domain of t to [0,1], and the number of residual points to be 400-800;

[0058] Step 3. Establish a deep neural network:

[0059] Establish a fully connected layer neural network including 6 hidden layers, and use the hyperbolic tangent function (Tanh) as the activation function to obtain a deep neural network model;

[0060] Step 4. Loss function design:

...

specific Embodiment approach 2

[0066] Embodiment 2: This embodiment differs from Embodiment 1 in that the number of residual points in Step 2 is 500.

specific Embodiment approach 3

[0067] Embodiment 3: This embodiment differs from Embodiment 1 or Embodiment 2 in that each hidden layer in Step 3 contains 40 neurons.

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Abstract

The invention discloses a multi-working-condition one-dimensional wave equation solving method based on a neural network, belongs to the field of seismic engineering, and aims to solve the problem that time and labor are consumed when the neural network is used for solving one-dimensional wave equations under different working conditions. The one-dimensional wave equation solving method comprises the following steps: 1, selecting a one-dimensional wave equation as an equation to be solved; 2, determining a solution domain and the number of residues of an input variable; 3, establishing a full connection layer neural network comprising six hidden layers; 4, designing a specific loss function; and 5, performing pre-training and fine training of the neural network. The invention provides the one-dimensional wave equation solving method based on the neural network by taking the wave velocity as input, so that the model can learn the influence of different working conditions on an equation solution, and the interpretability of the solving method is also improved by adding a generic equation and a stress condition on the premise of keeping high solving precision.

Description

technical field [0001] The invention belongs to the field of earthquake engineering, and in particular relates to a method for solving multi-working-condition one-dimensional wave equations based on a neural network and then realizing earthquake simulation. Background technique [0002] Accompanied by economic development, accelerated urbanization, and the emergence of megacities and urban agglomerations, all of these pose higher challenges to the seismic resilience of individual structures, building groups, and even the entire city. The establishment of the ground motion field is the prerequisite for the design and evaluation of the structural seismic toughness. How to quickly and accurately simulate the earthquake field is also a hot topic in the academic circles. The seismic field is closely related to the propagation of seismic waves in the medium. According to the representation theorem, ground motion can be expressed as the convolution of the Green's function and the...

Claims

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Application Information

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IPC IPC(8): G06F17/13G06N3/04G06N3/08G01V1/36
CPCG06F17/13G06N3/04G06N3/08G01V1/36G01V2210/675
Inventor 籍多发翟长海李晨曦温卫平
Owner HARBIN INST OF TECH
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