  Numerous polluted groundwater sites across the globe require an active
remediation strategy to restore natural environmental conditions and local
ecosystem. The Engineered Nano-particles (ENPs) have emerged as an efficient
reactive agent for the in-situ degradation of groundwater contaminants. While
the performance of these ENPs has been highly promising on the laboratory
scale, their application in real field case conditions is still limited. The
complex transport and retention mechanisms of ENPs hinder the development of an
efficient remediation strategy. Therefore, a predictive tool for understanding
the transport and retention behavior of ENPs is highly required. The existing
tools in the literature are dominated with numerical simulators, which have
limited flexibility and accuracy in the presence of sparse datasets. This work
uses a dynamic, weight-enabled Physics-Informed Neural Network (dw-PINN)
framework to model the nano-particle behavior within an aquifer. The result
from the forward model demonstrates the effective capability of dw-PINN in
accurately predicting the ENPs mobility. The model verification step shows that
the relative mean square error (MSE) of the predicted ENPs concentration using
dw-PINN converges to a minimum value of $1.3{e^{-5}}$. In the subsequent step,
the result from the inverse model estimates the governing parameters of ENPs
mobility with reasonable accuracy. The research demonstrates the tool's
capability to provide predictive insights for developing an efficient
groundwater remediation strategy.
