Theory-guided full convolutional neural network: An efficient surrogate model for inverse problems in subsurface contaminant transport

Volume: 157, Pages: 104051 - 104051
Published: Nov 1, 2021
Abstract
Identification of the location and strength of a contaminant source in an aquifer is a challenging but crucial task. Efficient surrogate models can be constructed to replace traditional time-consuming simulators while solving this inverse problem. In recent years, with the rapid development of machine learning (ML) algorithms, the artificial neural network (ANN) has been proven to be an efficient way for surrogate modeling. However, it may be...
Paper Details
Title
Theory-guided full convolutional neural network: An efficient surrogate model for inverse problems in subsurface contaminant transport
Published Date
Nov 1, 2021
Volume
157
Pages
104051 - 104051
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