Chemical engineering applications have been a source of challenging optimization
problems for over 50 years. For many chemical process systems, detailed steady state and …

Ultrathin meta-optics offer unmatched, multifunctional control of light. Next-generation optical
technologies, however, demand unprecedented performance. This will likely require design …

In this paper, a continuous time recurrent neural network (CTRNN) is developed to be used
in nonlinear model predictive control (NMPC) context. The neural network represented in a …

Recent advancements in inverse design approaches, exemplified by their large-scale
optimization of all geometrical degrees of freedom, have provided a significant paradigm …

This paper considers the numerical solution of optimal control problems based on ODEs. We
assume that an explicit Runge-Kutta method is applied to integrate the state equation in the …

In this paper an efficient algorithm to train general differential recurrent neural network
(DRNN) is developed. The trained network can be directly used in the nonlinear model …

Aim of this study is to compare two variants of the real-time iteration (RTI) scheme in
nonlinear model predictive control (NMPC): the standard RTI scheme as described in M …

The adjoint gradient method is well recognized for its efficiency in large-scale production
optimization. When implemented in a sequential quadratic programming (SQP) algorithm …

Prior to the proposals for Poisson likelihood models, the Lawrence Berkeley Laboratory had
proposed and investigated weighted least-squares (WLS) methods for SPECT (in 3D!) using …

In this paper, we use the calculus of variations to derive a sensitivity analysis for ordinary
differential equations with events. This sweeping gradient method (SGM) requires a forward …