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.

通过使用Bayesian Physics-Informed Neural Network（B-PINN）框架，该研究演示了该工具提供的预测性洞见，以开发高效的地下水修复策略。

贝叶斯物理推理神经网络用于受污染含水层中工程纳米颗粒迁移的正向和反向模拟