MoTIF uses HOSVD to separate multi-parametric unsteady flow data into modal components, applies GPR for parametric and spatial interpolation and RNN for temporal forecasting, achieving under 2% relative RMS error on laminar flow cases with varying Reynolds number and angle of attack.
Importance of input data normalization for the application of neural net- works to complex industrial problems
1 Pith paper cite this work, alongside 842 external citations. Polarity classification is still indexing.
1
Pith paper citing it
842
external citations · OpenAlex
fields
physics.flu-dyn 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems
MoTIF uses HOSVD to separate multi-parametric unsteady flow data into modal components, applies GPR for parametric and spatial interpolation and RNN for temporal forecasting, achieving under 2% relative RMS error on laminar flow cases with varying Reynolds number and angle of attack.