Many industrial processes belong to distributed parameter systems (DPS) that have strong
spatial–temporal dynamics. Modeling of DPS is difficult but essential to simulation, control …

The optimization of subsurface flow processes is important for many applications, including
oil field operations and the geological storage of carbon dioxide. These optimizations are …

A trajectory piecewise linearization (TPWL) procedure for the reduced-order modeling of two-
phase flow in subsurface formations is developed and applied. The method represents new …

We present a reduced-order modeling technique for subsurface multi-phase flow problems
building on the recently introduced deep residual recurrent neural network (DR …

The determination of optimal well settings is very demanding computationally because the
simulation model must be run many times during the course of the optimization. For this …

In this work the radial basis function neural network architecture is used to model the
dynamics of Distributed Parameter Systems (DPSs). Two pure data driving schemes which …

Nonlinear problems have drawn the attention of many researchers, engineers and scientists
as most of the real systems are inherently nonlinear in nature. These systems widely exist in …

Accurate solutions of distributed parameter systems may be represented as the sum of an
infinite series. Control design however, requires low-order models primarily due to …

Hydraulic fracturing is a technique for enhancing the extraction of oil and gas from deep
underground sources. Two important goals during this process are to achieve a final fracture …

Modeling of distributed parameter systems (DPSs) is very difficult because of their infinite-
dimensional, spatio-temporal nature and nonlinearities. A low-order, simple nonlinear and …