We present a physics‐informed deep neural network (DNN) method for estimating hydraulic
conductivity in saturated and unsaturated flows governed by Darcy's law. For saturated flow …

We present a physics informed deep neural network (DNN) method for estimating
parameters and unknown physics (constitutive relationships) in partial differential equation …

The exploration and exploitation of shale oil is an important aspect in the oil industry.
Seismic properties and well‐log data are essential to establish wave‐propagation models …

We present a new physics-informed machine learning approach for the inversion of partial
differential equation (PDE) models with heterogeneous parameters. In our approach, the …

We develop a physics‐informed machine learning approach for large‐scale data
assimilation and parameter estimation and apply it for estimating transmissivity and …

Seawater intrusion (SWI) is a complex process, where 3D modeling is often necessary in
order to monitor and manage the affected aquifers. Here, we present a synthetic study to test …

We present two approximate Bayesian inference methods for parameter estimation in partial
differential equation (PDE) models with space-dependent and state-dependent parameters …

Inverse modeling seeks model parameters given a set of observations. However, for
practical problems because the number of measurements is often large and the model …

Inverse problems often involve matching observational data using a physical model that
takes a large number of parameters as input. These problems tend to be under-constrained …

We present a model inversion algorithm, CKLEMAP, for data assimilation and parameter
estimation in partial differential equation models of physical systems with spatially …