An ensemble-variational framework approximates gradients via perturbed control vectors to optimize steady forcing in 2D cavity flows across quasi-periodic to chaotic regimes.
Flow, Turbulence and Combustion65(3/4), 393–415 (2000)
3 Pith papers cite this work. Polarity classification is still indexing.
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Quantized local reduced-order models paired with adjoint optimization reconstruct full trajectories in the chaotic Kuramoto-Sivashinsky equation up to 0.25 Lyapunov times with 3.5x speedup over full-order models.
Adjoint PINN surrogates are constructed to evolve runaway electron fluid moments and distributions for arbitrary initial conditions, achieving orders-of-magnitude speedup over conventional RE solvers with reported validation agreement.
citing papers explorer
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An Ensemble Variational approach for High-Dimensional Open-Loop Flow Control
An ensemble-variational framework approximates gradients via perturbed control vectors to optimize steady forcing in 2D cavity flows across quasi-periodic to chaotic regimes.
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Adjoint-based optimization with quantized local reduced-order models for spatiotemporally chaotic systems
Quantized local reduced-order models paired with adjoint optimization reconstruct full trajectories in the chaotic Kuramoto-Sivashinsky equation up to 0.25 Lyapunov times with 3.5x speedup over full-order models.
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Hierarchical Framework of Runaway Electrons using Deep Learning
Adjoint PINN surrogates are constructed to evolve runaway electron fluid moments and distributions for arbitrary initial conditions, achieving orders-of-magnitude speedup over conventional RE solvers with reported validation agreement.