  Modelling of systems where the full system information is unknown is an oft
encountered problem for various engineering and industrial applications, as
it's either impossible to consider all the complex physics involved or simpler
models are considered to keep within the limits of the available resources.
Recent advances in greybox modelling like the deep hidden physics models
address this space by combining data and physics. However, for most real-life
applications, model generalizability is a key issue, as retraining a model for
every small change in system inputs and parameters or modification in domain
configuration can render the model economically unviable. In this work we
present a novel enhancement to the idea of hidden physics models which can
generalize for changes in system inputs, parameters and domains. We also show
that this approach holds promise in system discovery as well and helps learn
the hidden physics for the changed system inputs, parameters and domain
configuration.
