William Kleiber

Predictive Modeling with Learned Constitutive Laws from Indirect Observations

The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes

Deep learning and process understanding for data-driven Earth system science

Taking the Human Out of the Loop: A Review of Bayesian Optimization

Machine learning of accurate energy-conserving molecular force fields

A hierarchical clustering method for multivariate geostatistical data

Short-term wind speed prediction model based on GA-ANN improved by VMD

Modeling Nonstationary Processes Through Dimension Expansion

Physics-informed machine learning

Methods for interpreting and understanding deep neural networks

Using Artificial Intelligence to Improve Real-Time Decision-Making for High-Impact Weather

Climatologies at high resolution for the earth’s land surface areas

An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach

A Bayesian kriging approach for blending satellite and ground precipitation observations

Cross-Covariance Functions for Multivariate Geostatistics

Nonstationary modeling for multivariate spatial processes

A Markov-Switching Vector Autoregressive Stochastic Wind Generator for Multiple Spatial and Temporal Scales

Matérn Cross-Covariance Functions for Multivariate Random Fields