A Review of Evaluation, Optimization and Synthesis of Energy Systems: Methodology and Application to Thermal Power Plants

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In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

This work studies the power density (PD) optimization in wind farms, and its sensitivity to the available area size. A novel genetic algorithm (PDGA) is introduced, which optimizes PD and the turbine layout, by self-adapting to the PD and to the solutions ...

Omnidirectional video streaming is usually implemented based on the representations of tiles, where the tiles are obtained by splitting the video frame into several rectangular areas and each tile is converted into multiple representations with different r ...

Within the context of contemporary machine learning problems, efficiency of optimization process depends on the properties of the model and the nature of the data available, which poses a significant problem as the complexity of either increases ad infinit ...

The design of an oil free turbocharger supported on herringbone grooved gas bearing was formulated as a multi-objective problem, which was solved by coupling a reduced order parametric model for gas bearing supported rotors with an evolutionary algorithm. ...

In Process Systems Engineering, computationally-demanding models are frequent and plentiful. Handling such complexity in an optimization framework in a fast and reliable way is essential, not only for generating meaningful solutions but also for providing ...

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision- and policy-making, and more, by comprehensively m ...

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

Detailed single neuron modeling is widely used to study neuronal functions. While cellular and functional diversity across the mammalian cortex is vast, most of the available computational tools are dedicated to the reproduction of a small set of specific ...

This work presents a new computational optimization framework for the robust control of parks of Wave Energy Converters (WEC) in irregular waves. The power of WEC parks is maximized with respect to the individual control damping and stiffness coefficients ...

Modern machine learning (ML) models are capable of impressive performances. However, their prowess is not due only to the improvements in their architecture and training algorithms but also to a drastic increase in computational power used to train them.|S ...

Designing turbocompressors is a complex and challenging task, as it involves balancing conflicting objectives such as efficiency, stability, and robustness against manufacturing deviations. This paper proposes an integrated design methodology for turbocomp ...

A Review of Evaluation, Optimization and Synthesis of Energy Systems: Methodology and Application to Thermal Power Plants

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Understanding generalization and robustness in modern deep learning

Wind farm power density optimization according to the area size using a novel self-adaptive genetic algorithm

Quality-Constrained Encoding Optimization for Omnidirectional Video Streaming

Universal and adaptive methods for robust stochastic optimization

Design and experimental investigation of a herringbone grooved gas bearing supported turbocharger

A combined genetic algorithm and active learning approach to build and test surrogate models in Process Systems Engineering

A comprehensive review of digital twin-part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

A universal workflow for creation, validation and generalization of detailed neuronal models

Robust optimization of control parameters for WEC arrays using stochastic methods

Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search

An Integrated Approach to Designing Robust Turbocompressors on Gas Bearings Through Surrogate Modeling and Constrained Multi-Objective Optimization