Data assimilation plays a dual role in advancing the “scientific” understanding and serving
as an “engineering tool” for the Earth system sciences. Land data assimilation (LDA) has …

Data assimilation, in its most comprehensive form, addresses the Bayesian inverse problem
of identifying plausible state trajectories that explain noisy or incomplete observations of …

Predicting the temperature, pressure, and permeability at depth is crucial for understanding
natural-state geothermal systems. As direct observations of these quantities are limited to …

Dependent data are ubiquitous in statistics and across various subject matter domains, with
dependencies across space, time, and variables. Basis expansions have proven quite …

Physics-informed neural networks (PINNs) have prevailed as differentiable simulators to
investigate flow in porous media. Despite recent progress PINNs have achieved, practical …

Abstract In recent years, Scientific Machine Learning (SciML) methods for solving partial
differential equations (PDEs) have gained wide popularity. Within such a paradigm, Physics …

We propose a multi-component approach for improving the training of the physics-informed
neural network (PINN) model for parabolic problems with a sharply perturbed initial …

Reservoir history matching refers to the process of continuously adjusting the parameters of
the reservoir model, so that its dynamic response will match the historical observation data …

We propose a physics-informed machine-learning method based on space-time-dependent
Karhunen-Loève expansions (KLEs) of the state variables and the residual least-square …

We propose the randomized physics-informed conditional Karhunen-Loève expansion
(rPICKLE) method for uncertainty quantification in high-dimensional inverse problems. In …