论文标题
连续方法：自适应侵入性降低订单模型闭合
Continuous Methods : Adaptively intrusive reduced order model closure
论文作者
论文摘要
减少的订单建模方法通常被用作降低工业应用中的模拟成本的平均值。尽管具有计算优势，但降低的订单模型（ROM）通常无法准确地再现现实生活中遇到的复杂动态。为了应对这一挑战，我们利用神经台面提出了一种基于时间连续记忆表述的新型ROM校正方法。最后，实验结果表明，我们提出的方法提供了很高的准确性，同时保留了降低模型固有的低计算成本。
Reduced order modeling methods are often used as a mean to reduce simulation costs in industrial applications. Despite their computational advantages, reduced order models (ROMs) often fail to accurately reproduce complex dynamics encountered in real life applications. To address this challenge, we leverage NeuralODEs to propose a novel ROM correction approach based on a time-continuous memory formulation. Finally, experimental results show that our proposed method provides a high level of accuracy while retaining the low computational costs inherent to reduced models.
