Revised: This Reviewed Preprint has been revised by the authors in response to the previous round of peer review; the eLife assessment and the public reviews have been updated where necessary by the ...

In this research work authors have experimentally validated a blend of Machine Learning and Nonlinear Model Predictive Control (NMPC) framework designed to track the temperature profile in a Batch ...

Abstract: We put forth a hybrid-computing solution to a class of constrained nonlinear optimization problems involving nonlinear cost and linear constraints. This is accomplished by realizing gradient ...

However, this capability comes at the expense of significant computational costs. NMPC necessitates solving nonlinear optimization problems at each control interval, which involve predicting future ...

Department of Mathematical Statistics and Differential Equations, Ivan Franko National University of Lviv, Lviv, Ukraine Problems without initial conditions for evolution equations and variational ...

To reduce transportation time, a discrete zeroing neural network (DZNN) method is proposed to solve the shortest path planning problem with a single starting point and a single target point. The ...

We put forth a hybrid-computing solution to a class of constrained nonlinear optimization problems involving nonlinear cost and linear constraints. This is accomplished by realizing gradient-flow ...

A framework based on advanced AI techniques can solve complex, computationally intensive problems faster and in a more more scalable way than state-of-the-art methods, according to a study led by ...