【摘 要】
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This talk concerns a low-rank approximation method for the model reduction of non-linear parametric dynamical systems.The proposed approach combines the con
【机 构】
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Ecole Centrale de Nantes
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This talk concerns a low-rank approximation method for the model reduction of non-linear parametric dynamical systems.The proposed approach combines the construction of a time dependent reduced space in which the full model is projected to derive the reduced dynamical system that takes into account the basis dynamic through a modified flux.Here,the reduced space basis is selected in a greedy fashion among a snapshot in parameter of the solution trajectories using a posteriori error estimate.
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