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Globally, there are many polluted groundwater sites that need an activeremediation plan for the restoration of local ecosystem and environment.Engineered nanoparticles (ENPs) have proven to be an effective reactive agentfor the in-situ degradation of pollutants in groundwater. While the performanceof these ENPs has been highly promising on the laboratory scale, theirapplication in real field case conditions is still limited. The complextransport and retention mechanisms of ENPs hinder the development of anefficient remediation strategy. Therefore, a predictive tool to comprehend thetransport and retention behavior of ENPs is highly required. The existing toolsin the literature are dominated with numerical simulators, which have limitedflexibility and accuracy in the presence of sparse datasets and the aquiferheterogeneity. This work uses a Bayesian Physics-Informed Neural Network(B-PINN) framework to model the nano-particles mobility within an aquifer. Theresult from the forward model demonstrates the effective capability of B-PINNin accurately predicting the ENPs mobility and quantifying the uncertainty. Theinverse model output is then used to predict the governing parameters for theENPs mobility in a small-scale aquifer. The research demonstrates thecapability of the tool to provide predictive insights for developing anefficient groundwater remediation strategy.

Bayesian Physics-Informed Neural Network for the Forward and Inverse Simulation of Engineered Nano-particles Mobility in a Contaminated Aquifer