Physics informed neural networks for an inverse problem in peridynamic models
Deep learning is a powerful tool for solving data driven differential problems and has come
out to have successful applications in solving direct and inverse problems described by …
out to have successful applications in solving direct and inverse problems described by …
[HTML][HTML] State Dependent Riccati for dynamic boundary control to optimize irrigation in Richards' equation framework
We present an approach for the optimization of irrigation in a Richards' equation framework.
We introduce a proper cost functional, aimed at minimizing the amount of water provided by …
We introduce a proper cost functional, aimed at minimizing the amount of water provided by …
[PDF][PDF] Analysis of a nonlinear problem involving discrete and proportional delay with application to Houseflies model
This manuscript established a comprehensive analysis of a general class of fractional order
delay differential equations with Caputo-Fabrizio fractional derivative (CFFD). Functional …
delay differential equations with Caputo-Fabrizio fractional derivative (CFFD). Functional …
Stabilized explicit peer methods with parallelism across the stages for stiff problems
G Pagano - Applied Numerical Mathematics, 2025 - Elsevier
In this manuscript, we propose a new family of stabilized explicit parallelizable peer methods
for the solution of stiff Initial Value Problems (IVPs). These methods are derived through the …
for the solution of stiff Initial Value Problems (IVPs). These methods are derived through the …
[HTML][HTML] Convergence analysis of a spectral numerical method for a peridynamic formulation of Richards' equation
We study the implementation of a Chebyshev spectral method with forward Euler integrator
proposed in Berardi et al.(2023) to investigate a peridynamic nonlocal formulation of …
proposed in Berardi et al.(2023) to investigate a peridynamic nonlocal formulation of …
[HTML][HTML] Investigating neural networks with groundwater flow equation loss
Abstract Physics-Informed Neural Networks (PINNs) are considered a powerful tool for
solving partial differential equations (PDEs), particularly for the groundwater flow (GF) …
solving partial differential equations (PDEs), particularly for the groundwater flow (GF) …
[HTML][HTML] Impact of collocation point sampling techniques on PINN performance in groundwater flow predictions
Abstract Physics-Informed Neural Networks (PINNs) represent a promising methodology for
addressing partial differential equations in scientific computing. This study examines …
addressing partial differential equations in scientific computing. This study examines …
A feedback control strategy for optimizing irrigation in a Richards' Equation framework
We present an approach for the optimization of irrigation in a Richards' equation framework.
We introduce a proper cost functional, aimed at minimizing the amount of water provided by …
We introduce a proper cost functional, aimed at minimizing the amount of water provided by …