Subsurface remediation often involves reconstruction of contaminant release history from
sparse observations of solute concentration. Markov Chain Monte Carlo (MCMC), the most …

Statistical (machine learning) tools for equation discovery require large amounts of data that
are typically computer generated rather than experimentally observed. Multiscale modeling …

Field‐scale properties of fractured rocks play a crucial role in many subsurface applications,
yet methodologies for identification of the statistical parameters of a discrete fracture network …

Identification of a heterogeneous conductivity field and reconstruction of a contaminant
release history are key aspects of subsurface remediation. These two goals are achieved by …

We introduce a mathematical formulation of feature-informed data assimilation (FIDA). In
FIDA, the information about feature events, such as shock waves, level curves, wavefronts …

The ability to quantify evapotranspiration (ET) is crucial for smart agriculture and sustainable
groundwater management. Efficient ET estimation strategies often rely on the vertical‐flow …

Evapotranspiration is arguably the least quantified component of the hydrologic cycle. We
propose two complementary strategies for estimation of evapotranspiration rates and root …

The data-aware method of distributions (DAMD) is a low-dimensional data assimilation
procedure to forecast the behavior of dynamical systems described by differential equations …

It is a long held conjecture in the connection between information geometry (IG) and
thermodynamics that the curvature endowed by IG diverges at phase transitions. Recent …

Ambiguity sets of probability distributions are used to hedge against uncertainty about the
true probabilities of uncertain inputs and random quantities of interest (QoIs). When …