A neural network that approximates virtual element basis functions from boundary data yields a stabilization-free polygonal discretization that converges on linear and nonlinear elasticity benchmarks.
A 3D-1D Virtual Element Method for Modeling Root Water Uptake
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abstract
An optimization-based strategy is proposed for coupling three-dimensional and one-dimensional problems (3D-1D coupling) in the context of soil-root interaction simulations. This strategy, originally designed to tackle generic 3D-1D coupled problems with discontinuous solutions, is here extended to the case of non-linear problems and applied, for the first time, along with a virtual element discretization of the 3D soil sample. This further enhances the capability of the method to handle geometrical complexities, allowing to easily mesh domains characterized, for instance, by the presence of stones and other impervious obstacles of arbitrary shape. A discrete-hybrid tip-tracking strategy is adopted to model both the root growth and the evolution in time of the water flux, the pressure head and the water content, both in the roots and in the surrounding soil sample. By choosing proper rules for the generation of branches, realistic root-network configurations are obtained. Several numerical examples are proposed, proving both the accuracy of the adopted method and its applicability in realistic and large scale simulations.
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The Neural Approximated Virtual Element Method for Elasticity Problems
A neural network that approximates virtual element basis functions from boundary data yields a stabilization-free polygonal discretization that converges on linear and nonlinear elasticity benchmarks.