Fast and accurate identification of unknown pollution sources is a crucial and challenging
task in water resources management, in which the characteristics of unknown pollution …

Identification of a groundwater contaminant source simultaneously with the hydraulic
conductivity in highly heterogeneous media often results in a high‐dimensional inverse …

This study reports on two strategies for accelerating posterior inference of a highly
parameterized and CPU‐demanding groundwater flow model. Our method builds on …

Surrogate models are commonly used in Bayesian approaches such as Markov Chain
Monte Carlo (MCMC) to avoid repetitive CPU‐demanding model evaluations. However, the …

Parameter estimation for reactive transport models (RTMs) is important in improving their
predictive capacity for accurately simulating subsurface hydrogeochemical processes. This …

In this study, an efficient full Bayesian approach is developed for the optimal sampling well
location design and source parameters identification of groundwater contaminants. An …

Forty years and 157 papers later, research on contaminant source identification has grown
exponentially in number but seems to be stalled concerning advancement towards the …

Abstract Model-based optimization plays a central role in energy system design and
management. The complexity and high-dimensionality of many process-level models …

Effective fire detection provides early warnings and key information for first responders and
people trapped insides. The idea of integrating sensor data and fire modeling presents a …

Increasing the precision of groundwater pollution source identification (GPSI) is crucial for
groundwater pollution control and risk management. Bayesian theory based on the Markov …