Web: http://arxiv.org/abs/2209.06642

Sept. 15, 2022, 1:11 a.m. | Luana P. Queiroz, Carine M. Rebello, Erber A. Costa, Vinicius V. Santana, Alirio E. Rodrigues, Ana M. Ribeiro, Idelfonso B. R. Nogueira

cs.LG updates on arXiv.org arxiv.org

Scientific machine learning (SciML) is a field of increasing interest in
several different application fields. In an optimization context, SciML-based
tools have enabled the development of more efficient optimization methods.
However, implementing SciML tools for optimization must be rigorously evaluated
and performed with caution. This work proposes the deductions of a robustness
test that guarantees the robustness of multiobjective SciML-based optimization
by showing that its results respect the universal approximator theorem. The
test is applied in the framework of a …

arxiv machine machine learning math optimization robustness theorem

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