Data-Driven Surrogate Modeling for the Flammability Reduction System

What is it about?

This paper uses Deep Learning as a surrogate-modelling technique to accurately model the outputs of a flammability reduction system. This model uses layers such as long short-term memory layers to accurately capture the time-dependency between inputs and outputs. The resutls shows that the designed surrogate model is about 10000 times faster than conventional simulators.

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Why is it important?

The significance of Artificial Intelligence is explored in this paper, where its prowess is showcased specifically within the realm of aerospace engineering.