In the literature, machine learning (ML) and artificial intelligence (AI) applications tend to
start with examples that are irrelevant to process engineers (eg classification of images …

The full optimization of the design and operation of instruments whose functioning relies on
the interaction of radiation with matter is a super-human task, due to the large dimensionality …

In the context of science, the well-known adage" a picture is worth a thousand words" might
well be" a model is worth a thousand datasets." In this manuscript we introduce the SciML …

Physics-Informed Neural Networks (PINNs) have emerged recently as a promising
application of deep neural networks to the numerical solution of nonlinear partial differential …

Bi-Level Optimization (BLO) is originated from the area of economic game theory and then
introduced into the optimization community. BLO is able to handle problems with a …

Abstract Physics-Informed Neural Networks (PINNs) have emerged recently as a promising
application of deep neural networks to the numerical solution of nonlinear partial differential …

Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine
learning. Automatic differentiation (AD), also called algorithmic differentiation or simply" auto …

JuMP is an open-source modeling language that allows users to express a wide range of
optimization problems (linear, mixed-integer, quadratic, conic-quadratic, semidefinite, and …

ABSTRACT Neural Ordinary Differential Equations (ODEs) are a promising approach to
learn dynamical models from time-series data in science and engineering applications. This …

The adjoint method provides an efficient way to compute sensitivities for system models with
a large number of inputs. However, implementing the adjoint method requires significant …