Bayesian Physics-Informed Neural Network for the Forward and Inverse
  Simulation of Engineered Nano-particles Mobility in a Contaminated Aquifer
Shikhar Nilabh Fidel Grandia
Globally, there are many polluted groundwater sites that need an active
remediation plan for the restoration of local ecosystem and environment.
Engineered nanoparticles (ENPs) have proven to be an effective reactive agent
for the in-situ degradation of pollutants in groundwater. 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 to comprehend 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 and the aquifer
heterogeneity. This work uses a Bayesian Physics-Informed Neural Network
(B-PINN) framework to model the nano-particles mobility within an aquifer. The
result from the forward model demonstrates the effective capability of B-PINN
in accurately predicting the ENPs mobility and quantifying the uncertainty. The
inverse model output is then used to predict the governing parameters for the
ENPs mobility in a small-scale aquifer. The research demonstrates the
capability of the tool to provide predictive insights for developing an
efficient groundwater remediation strategy.