4,786 research outputs found
Approximating the Solution of Surface Wave Propagation Using Deep Neural Networks
Partial differential equations formalise the understanding of the behaviour of the physical world that humans acquire through experience and observation. Through their numerical solution, such equations are used to model and predict the evolution of dynamical systems. However, such techniques require extensive computational resources and assume the physics are prescribed \textit{a priori}. Here, we propose a neural network capable of predicting the evolution of a specific physical phenomenon: propagation of surface waves enclosed in a tank, which, mathematically, can be described by the Saint-Venant equations. The existence of reflections and interference makes this problem non-trivial. Forecasting of future states (i.e. spatial patterns of rendered wave amplitude) is achieved from a relatively small set of initial observations. Using a network to make approximate but rapid predictions would enable the active, real-time control of physical systems, often required for engineering design. We used a deep neural network comprising of three main blocks: an encoder, a propagator with three parallel Long Short-Term Memory layers, and a decoder. Results on a novel, custom dataset of simulated sequences produced by a numerical solver show reasonable predictions for as long as 80 time steps into the future on a hold-out dataset. Furthermore, we show that the network is capable of generalising to two other initial conditions that are qualitatively different from those seen at training time
Characterizing Evaporation Ducts Within the Marine Atmospheric Boundary Layer Using Artificial Neural Networks
We apply a multilayer perceptron machine learning (ML) regression approach to
infer electromagnetic (EM) duct heights within the marine atmospheric boundary
layer (MABL) using sparsely sampled EM propagation data obtained within a
bistatic context. This paper explains the rationale behind the selection of the
ML network architecture, along with other model hyperparameters, in an effort
to demystify the process of arriving at a useful ML model. The resulting speed
of our ML predictions of EM duct heights, using sparse data measurements within
MABL, indicates the suitability of the proposed method for real-time
applications.Comment: 13 pages, 7 figure
Surrogate modelling and uncertainty quantification based on multi-fidelity deep neural network
To reduce training costs, several Deep neural networks (DNNs) that can learn
from a small set of HF data and a sufficient number of low-fidelity (LF) data
have been proposed. In these established neural networks, a parallel structure
is commonly proposed to separately approximate the non-linear and linear
correlation between the HF- and LF data. In this paper, a new architecture of
multi-fidelity deep neural network (MF-DNN) was proposed where one subnetwork
was built to approximate both the non-linear and linear correlation
simultaneously. Rather than manually allocating the output weights for the
paralleled linear and nonlinear correction networks, the proposed MF-DNN can
autonomously learn arbitrary correlation. The prediction accuracy of the
proposed MF-DNN was firstly demonstrated by approximating the 1-, 32- and
100-dimensional benchmark functions with either the linear or non-linear
correlation. The surrogating modelling results revealed that MF-DNN exhibited
excellent approximation capabilities for the test functions. Subsequently, the
MF DNN was deployed to simulate the 1-, 32- and 100-dimensional aleatory
uncertainty propagation progress with the influence of either the uniform or
Gaussian distributions of input uncertainties. The uncertainty quantification
(UQ) results validated that the MF-DNN efficiently predicted the probability
density distributions of quantities of interest (QoI) as well as the
statistical moments without significant compromise of accuracy. MF-DNN was also
deployed to model the physical flow of turbine vane LS89. The distributions of
isentropic Mach number were well-predicted by MF-DNN based on the 2D Euler flow
field and few experimental measurement data points. The proposed MF-DNN should
be promising in solving UQ and robust optimization problems in practical
engineering applications with multi-fidelity data sources
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