  Deep neural operators are recognized as an effective tool for learning
solution operators of complex partial differential equations (PDEs). As
compared to laborious analytical and computational tools, a single neural
operator can predict solutions of PDEs for varying initial or boundary
conditions and different inputs. A recently proposed Wavelet Neural Operator
(WNO) is one such operator that harnesses the advantage of time-frequency
localization of wavelets to capture the manifolds in the spatial domain
effectively. While WNO has proven to be a promising method for operator
learning, the data-hungry nature of the framework is a major shortcoming. In
this work, we propose a physics-informed WNO for learning the solution
operators of families of parametric PDEs without labeled training data. The
efficacy of the framework is validated and illustrated with four nonlinear
spatiotemporal systems relevant to various fields of engineering and science.
