算子推理的可微求解器方法
A Differentiable Solver Approach to Operator Inference
摘要：
模型降阶是数字孪生环境下工业应用的关键技术。关键要求是非侵入性、物理感知以及鲁棒性和可用性。基于最小二乘最小化和离散经验插值方法的运算符推断捕获了这些要求中的大部分，尽管所需的正则化限制了可用性。在这方面的贡献，我们重新制定的约束最优化问题允许放松所需的正则化的算子推理的问题。其结果是一个强大的模型降阶方法，为现实世界的工业应用，这是沿着一个动态复杂的三维冷却过程的多管式反应器，使用商业软件包进行验证。
英文摘要：
Model Order Reduction is a key technology for industrial applications in the context of digital twins. Key requirements are non-intrusiveness, physics-awareness, as well as robustness and usability. Operator inference based on least-squares minimization combined with the Discrete Empirical Interpolation Method captures most of these requirements, though the required regularization limits usability. Within this contribution we reformulate the problem of operator inference as a constrained optimization problem allowing to relax on the required regularization. The result is a robust model order reduction approach for real-world industrial applications, which is validated along a dynamics complex 3D cooling process of a multi-tubular reactor using a commercial software package.
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