We present an algorithm for learning operators between Banach spaces, based on the use of Leray-Schauder mappings to learn a finite-dimensional approximation of compact subspaces. We show that the resulting method is a universal approximator of (possibly nonlinear) operators. We demonstrate the efficiency of the approach on two benchmark datasets showing it achieves results comparable to state of the art models.

翻译：本文提出了一种基于 Leray-Schauder 映射学习 Banach 空间之间算子的算法，该方法通过 Leray-Schauder 映射来学习紧子空间的有限维逼近。我们证明了所得到的算法是（可能非线性的）算子的通用逼近器。我们在两个基准数据集上展示了该方法的效率，结果表明其性能可与当前最先进的模型相媲美。