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.
A novel tensor-based modal decomposition method for reduced order modeling and optimal sparse sensor placement,
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Koopman models identified via meta-heuristic EDMD from engine simulations enable an adaptive MPC with disturbance observer and a feedback linearization controller that achieve comparable steady-state performance with the adaptive version showing superior robustness under varying conditions.
citing papers explorer
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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.
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Koopman-Based Nonlinear Identification and Adaptive Control of a Turbofan Engine
Koopman models identified via meta-heuristic EDMD from engine simulations enable an adaptive MPC with disturbance observer and a feedback linearization controller that achieve comparable steady-state performance with the adaptive version showing superior robustness under varying conditions.