Feb. 9, 2024, 5:44 a.m. | Tomoki Koike Elizabeth Qian

cs.LG updates on arXiv.org arxiv.org

Many-query computations, in which a computational model for an engineering system must be evaluated many times, are crucial in design and control. For systems governed by partial differential equations (PDEs), typical high-fidelity numerical models are high-dimensional and too computationally expensive for the many-query setting. Thus, efficient surrogate models are required to enable low-cost computations in design and control. This work presents a physics-preserving reduced model learning approach that targets PDEs whose quadratic operators preserve energy, such as those arising in …

computational control cs.lg cs.na design differential energy engineering fidelity inference math.ds math.na numerical query systems

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