In this section, we present a brief overview of the deep hidden physics model (DH-PMs) , which deals with system modeling when the full governing equations are not known. DHPMs leverage a pair of NNs or other methods to account for unknown physics, enabling robust forward and inverse modeling . Unlike physics-informed neural networks (PINNs) , which utilize a single network and are limited by available system information, DHPMs incorporate a second network to capture unknown physics, enhancing model accuracy and applicability. Several recent works also deal with a combination of Gaussian processes , and a combination of NNs and sparse regression to model systems with partial information of physics. Without any loss of generality, consider a non-linear time-dependent PDE of the form: