2023
DOI: 10.1186/s40323-023-00254-y
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Deep convolutional architectures for extrapolative forecasts in time-dependent flow problems

Pratyush Bhatt,
Yash Kumar,
Azzeddine Soulaïmani

Abstract: Physical systems whose dynamics are governed by partial differential equations (PDEs) find numerous applications in science and engineering. The process of obtaining the solution from such PDEs may be computationally expensive for large-scale and parameterized problems. In this work, deep learning techniques developed especially for time-series forecasts, such as LSTM and TCN, or for spatial-feature extraction such as CNN, are employed to model the system dynamics for advection-dominated problems. This paper p… Show more

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