A long short-term memory based deep learning algorithm for seismic response uncertainty quantification
A Kundu, S Ghosh, S Chakraborty - Probabilistic Engineering Mechanics, 2022 - Elsevier
The application of metamodeling technique to overcome computational challenge of Monte
Carlo simulation (MCS) technique for response uncertainty quantification under stochastic
earthquake load is a difficult task due to the high-dimensional nature of stochastic load.
Recent developments in the sequential models for forecasting and prediction have opened
a new avenue in this regard. Various deep learning algorithms, particularly the convolutional
neural network and recurrent neural network are quite suitable for response uncertainty …
Carlo simulation (MCS) technique for response uncertainty quantification under stochastic
earthquake load is a difficult task due to the high-dimensional nature of stochastic load.
Recent developments in the sequential models for forecasting and prediction have opened
a new avenue in this regard. Various deep learning algorithms, particularly the convolutional
neural network and recurrent neural network are quite suitable for response uncertainty …
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