With the increasing trend of solving more complex and integrated optimization problems,
there is a need for developing process models that are sufficiently accurate as well as …

Recent developments in data science and machine learning have inspired a new wave of
research into data-driven modeling for mathematical optimization of process applications …

We introduce the concept of decision‐focused surrogate modeling for solving
computationally challenging nonlinear optimization problems in real‐time settings. The …

Simulation-based optimization using surrogate models enables decision-making through
the exchange of data from high-fidelity models and development of approximations. Many …

In real-world optimization, it is often expensive to evaluate the quality of a candidate
solution. The costs may be due to run-time of a complex computer simulation, time required …

Surrogate models are used to map input data to output data when the actual relationship
between the two is unknown or computationally expensive to evaluate for sensitivity …

Simulation models are widely used in practice to facilitate decision making in a complex,
dynamic and stochastic environment. But they are computationally expensive to execute and …

The idea of using a simpler surrogate to represent a complex phenomenon has gained
increasing popularity over past three decades. Due to their ability to exploit the black-box …

Many industrial engineering problems involve complex formulations and are assisted by
simulation tools. Although these tools provide highly accurate solutions, they may not be …

The development of pharmaceutical manufacturing processes has been facilitated by recent
advances in the process simulation area. However, since the simulations are usually …