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In superstructure optimization of processes and energy systems, the design space is defined as the combination of unit considerations, process conditions and model parameters that might be subjected to uncertainty. Most of the time, decision makers are not ...
Elsevier Science2022
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Simulation-based optimization models are widely applied to find optimal operating conditions of processes. Often, computational challenges arise from model complexity, making the generation of reliable design solutions difficult. We propose an algorithm fo ...
A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...
Traditional priority schemes reduce delays for some by increasing those of others. Yet, this might not be a necessity. Several works published over the last two decades have shown for a stylized set-up with homogeneous users that dynamic priority scheme ma ...
We study the consistency of the estimator in spatial regression with partial differential equa-tion (PDE) regularization. This new smoothing technique allows to accurately estimate spatial fields over complex two-dimensional domains, starting from noisy ob ...
2020
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Regularization, filtering, and denoising of biomedical images requires the use of appropriate filters and the adoption of efficient regularization criteria. It has been shown that the Stein’s Unbiased Risk Estimate (SURE) can be used as a proxy for the mea ...
Many decision problems in science, engineering, and economics are affected by uncertainty, which is typically modeled by a random variable governed by an unknown probability distribution. For many practical applications, the probability distribution is onl ...
Energy proportionality and workload consolidation are important objectives towards increasing efficiency in large-scale datacenters. Our work focuses on achieving these goals in the presence of applications with microsecond-scale tail latency requirements. ...
Linear inverse problems are ubiquitous. Often the measurements do not follow a Gaussian distribution. Additionally, a model matrix with a large condition number can complicate the problem further by making it ill-posed. In this case, the performance of pop ...
We consider the numerical approximation of the stochastic Darcy problem with log-normal permeability field and propose a novel Multi Level Monte Carlo (MLMC) approach with a control variate variance reduction technique on each level. We model the log-perme ...
