<ul><li>Operator networks are designed to approximate nonlinear operators, which provide mappings between infinite-dimensional spaces.</li><li>The radial basis operator network (RBON) is introduced as the first operator network capable of learning an operator in both the time domain and frequency domain when adjusted to accept complex-valued inputs.</li><li>The RBON exhibits a small $L^2$ relative test error for in- and out-of-distribution data, with less than $1	imes 10^{-7}$ in some benchmark cases.</li><li>The RBON maintains small error on out-of-distribution data from entirely different function classes compared to the training data.</li></ul>

Radial Basis Operator Networks

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