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In this paper, we consider the problem of Gaussian process (GP) optimization with an added robustness requirement: The returned point may be perturbed by an adversary, and we require the function value to remain as high as possible even after this perturba ...
Optimal operation of chemical processes is key for meeting productivity, quality, safety and environmental objectives. Both model-based and data-driven schemes are used to compute optimal operating conditions [1]: - The model-based techniques are intu ...
The desire to operate chemical processes in a safe and economically optimal way has motivated the development of so-called real-time optimization (RTO) methods [1]. For continuous processes, these methods aim to compute safe and optimal steady-state set ...
This paper provides expressions for the largest and smallest solution of a global optimization problem using an adjoint variable which represents the available one-sided improvements up to the interval “horizon”. Interpreting the problem in terms of optima ...
Compressor impellers for mass-market turbochargers are die-casted and machined with an aim to achieve high dimensional accuracy and acquire specific performance. However, manufacturing uncertainties result in dimensional deviations causing incompatible ope ...
This paper provides expressions for solutions of a one-dimensional global optimization problem using an adjoint variable which represents the available one-sided improvements up to the interval "horizon." Interpreting the problem in terms of optimal stoppi ...
In this paper we present a certified reduced basis (RB) framework for the efficient solution of PDE-constrained parametric optimization problems. We consider optimization problems (such as optimal control and optimal design) governed by elliptic PDEs and i ...
In this paper, we propose a model order reduction framework for parametrized quadratic optimization problems constrained by nonlinear stationary PDEs. Once the solutions of the optimization problem are characterized as the solutions of the corresponding op ...
A short technical subsection for Gaussian Process learning and Uncertainty propagation is presented as required in applications like Model Predictive Control, machine learning and optimization. ...
This review discusses some issues related to the use of simulation in transportation analysis. Potential pitfalls are identified and discussed. An overview of some methods relevant to the use of an advanced simulation tool in an optimization context is als ...
