A Regularized Physics-Informed Neural Network to support Data-Driven Nonlinear Constrained Optimization
Diego Armando Perez-Rosero,
Andrés Marino Álvarez-Meza,
Germán Castellanos-Dominguez
Abstract:Nonlinear optimization (NOPT) is a meaningful tool for solving complex tasks in fields like engineering, economics, and operations research, among others. However, NOPT has problems when it comes to dealing with data variability and noisy input measurements that lead to incorrect solutions. Furthermore, nonlinear constraints may result in outcomes that are either infeasible or suboptimal, such as non-convex optimization. This paper introduces a novel regularized physics-informed neural network (RPINN) framewor… Show more
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