Mathematical optimization

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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 ...

In this account, we discuss the use of genetic algorithms in the inverse design process of homogeneous catalysts for chemical transformations. We describe the main components of evolutionary experiments, specifically the nature of the fitness function to o ...

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. ...

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 ...

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 ...

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 ...

Organocatalysis has evolved significantly over the last decades, becoming a pillar of synthetic chemistry, but traditional theoretical approaches based on quantum mechanical computations to investigate reaction mechanisms and provide rationalizations of ca ...

Reaction optimization is challenging and traditionally delegated to domain experts who iteratively pro-pose increasingly optimal experiments. Problematically, the reaction landscape is complex and often requires hundreds of experiments to reach convergence ...

We introduce a reconfigurable medium for the manipulation of elastic propagation properties of Lamb waves. It is based on a shape memory polymer (SMP) with temperature-dependent Young's modulus. Waves are excited by a laser pulse and detected by a laser vi ...

We introduce a computational pipeline for simulating and designing C-shells, a new class of planar-to-spatial deployable linkage structures. A C-shell is composed of curved flexible beams connected at rotational joints that can be assembled in a stress-fre ...

Objective. Peripheral nerve interfaces have the potential to restore sensory, motor, and visceral functions. In particular, intraneural interfaces allow targeting deep neural structures with high selectivity, even if their performance strongly depends upon ...

Mathematical optimization

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

Genetic Algorithms for the Discovery of Homogeneous Catalysts

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

Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search

Universal and adaptive methods for robust stochastic optimization

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

Enriching the Computational Toolbox for Organocatalysis

Bayesian Optimization for Chemical Reactions

Optimization of elastic wave propagation in a reconfigurable medium by genetic algorithms with adaptive mutation probability

C-shells: Deployable Gridshells with Curved Beams

Combining biophysical models and machine learning to optimize implant geometry and stimulation protocol for intraneural electrodes