Making Bayesian Inference and Predictions More Realistic: A Sliding Time-Window Approach

Development of a Combined Machine Learning and Physics-based Approach to Reduce Hydrologic Model Spin-up Time

Optimized Predictive Coverage by Averaging Time‐Windowed Bayesian Distributions

Estimating catchment-wide total groundwater storage via space-time kriging provides calibration data for catchment-scale groundwater balance models

Coordinated Optimization of Water Supply Systems with Evolving Energy Systems

Learning PFAS mechanisms with a Finite Volume Neural Network (FINN)

Randomizing fracture models through energy-based direction sampling

Information‐Theoretic Scores for Bayesian Model Selection and Similarity Analysis: Concept and Application to a Groundwater Problem