Elena Raponi

Elena Raponi
Technische Universität München | Sorbonne Unviersity · Chair of Computational Mechanics

Ph.D. in Applied Mathematics

About

26
Publications
5,506
Reads
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161
Citations
Citations since 2017
25 Research Items
161 Citations
20172018201920202021202220230102030405060
20172018201920202021202220230102030405060
20172018201920202021202220230102030405060
20172018201920202021202220230102030405060
Additional affiliations
November 2017 - present
University of Camerino
Position
  • PhD Student
October 2015 - October 2017
University of Camerino
Position
  • Master's Student
October 2012 - July 2015
University of Camerino
Position
  • Student

Publications

Publications (26)
Article
Understanding the dynamics of car-to-pedestrian-collisions (CPCs) is critical to reducing fatalities. Data collected from traffic accidents are insufficient in quantity and quality to support the reconstruction of the kinematics and thus to draw empirical conclusions. Due to the high costs associated with conducting physical CPC tests, numerical si...
Preprint
Full-text available
Bayesian optimization (BO) algorithms form a class of surrogate-based heuristics, aimed at efficiently computing high-quality solutions for numerical black-box optimization problems. The BO pipeline is highly modular, with different design choices for the initial sampling strategy, the surrogate model, the acquisition function (AF), the solver used...
Preprint
Full-text available
Bayesian Optimization (BO) is a powerful, sample-efficient technique to optimize expensive-to-evaluate functions. Each of the BO components, such as the surrogate model, the acquisition function (AF), or the initial design, is subject to a wide range of design choices. Selecting the right components for a given optimization task is a challenging ta...
Preprint
p>Explainable Artificial Intelligence (XAI) is an increasingly important field of research required to bring AI to the next level in real-world applications. Global sensitivity analysis methods play an important role in XAI, as they can provide an understanding of which (groups of) parameters have high influence in the predictions of machine learni...
Preprint
Full-text available
p>Explainable Artificial Intelligence (XAI) is an increasingly important field of research required to bring AI to the next level in real-world applications. Global sensitivity analysis methods play an important role in XAI, as they can provide an understanding of which (groups of) parameters have high influence in the predictions of machine learni...
Article
Quantum annealing is a heuristic quantum optimization algorithm that can be used to solve combinatorial optimization problems. In recent years, advances in quantum technologies have enabled the development of small- and intermediate-scale quantum processors that implement the quantum annealing algorithm for programmable use. Specifically, quantum a...
Chapter
Bayesian Optimization (BO) is a surrogate-based global optimization strategy that relies on a Gaussian Process regression (GPR) model to approximate the objective function and an acquisition function to suggest candidate points. It is well-known that BO does not scale well for high-dimensional problems because the GPR model requires substantially m...
Article
Full-text available
Explainable Artificial Intelligence (XAI) is an increasingly important field of research required to bring AI to the next level in real-world applications. Global sensitivity analysis methods play an important role in XAI, as they can provide an understanding of which (groups of) parameters have high influence in the predictions of machine learning...
Preprint
Full-text available
Bayesian Optimization (BO) is a surrogate-based global optimization strategy that relies on a Gaussian Process regression (GPR) model to approximate the objective function and an acquisition function to suggest candidate points. It is well-known that BO does not scale well for high-dimensional problems because the GPR model requires substantially m...
Article
Since natural fiber composites have a high potential as an alternative to synthetic materials, their mechanical properties must be investigated under different loading modes. In this paper, flax, basalt and hybrid flax-basalt/epoxy resin composite laminates are experimentally characterized and their behavior under low-velocity impact conditions is...
Preprint
Full-text available
Quantum annealing is a heuristic quantum optimization algorithm that can be used to solve combinatorial optimization problems. In recent years, advances in quantum technologies have enabled the development of small- and intermediate-scale quantum processors that implement the quantum annealing algorithm for programmable use. Specifically, quantum a...
Article
Numerical simulations for crashworthiness require the definition of material properties that are not always predictable with standard experimental tests. This paper deals with the numerical optimization of a thermoplastic composite material model. The component is a vehicle impact attenuator made of an innovative All-PP (PolyPropylene) composite ma...
Chapter
Full-text available
Topology Optimization (TO) represents a relevant tool in the design of mechanical structures and, as such, it is currently used in many industrial applications. However, many TO optimization techniques are still questionable when applied to crashworthiness optimization problems due to their complexity and lack of gradient information. The aim of th...
Article
Polymer composite materials are widespread in most industrial fields, such as automotive, civil engineering, marine, wind energy, packaging and sports, and their demand is increasing since they have the potential of combining high mechanical properties with the lightness of the components. However, due to their non-biodegradable nature, they are no...
Article
Full-text available
In this work, we present an experimental analysis and a numerical optimization study for the material parameter identification of an impact attenuator made of a brand new All-PP thermoplastic composite material subjected to an axial impact load. After an experimental characterization of the material, a Finite Element (FE) numerical model of the imp...
Chapter
Full-text available
Bayesian Optimization (BO) is a surrogate-assisted global optimization technique that has been successfully applied in various fields, e.g., automated machine learning and design optimization. Built upon a so-called infill-criterion and Gaussian Process regression (GPR), the BO technique suffers from a substantial computational complexity and hampe...
Preprint
Bayesian Optimization (BO) is a surrogate-assisted global optimization technique that has been successfully applied in various fields, e.g., automated machine learning and design optimization. Built upon a so-called infill-criterion and Gaussian Process regression (GPR), the BO technique suffers from a substantial computational complexity and hampe...
Article
Full-text available
Natural fiber composites have the potential to be widely applied as an alternative to or in combination with glass fiber composites in sustainable energy-absorbing structures. This study investigates the behavior of hemp fiber-reinforced vinylester composites when subjected to low-velocity impact loading by using an instrumented falling weight impa...
Presentation
Full-text available
This work deals with the exploration and optimization of the parameters characterizing the mechanical performance of a composite impact attenuator for a Formula SAE racing car. In order to maximize the specific energy absorption (SEA) capability of the crushed material while maintaining a stable collapse and fitting into a required envelope, the an...
Article
Full-text available
The wind energy market requires reliable wind turbines with a long and efficient working life, able to generate energy without interruption, at the lowest investment and operating cost. The current material systems used for making wind turbine blades are in majority based on glass fibres and epoxy resins. These thermoset polymer composites with syn...
Article
Over the recent decades, Topology Optimization (TO) has become an important tool in the design and analysis of mechanical structures. Although structural TO is already used in many industrial applications, it needs much more investigation in the context of vehicle crashworthiness. Indeed, crashworthiness optimization problems present strong nonline...
Conference Paper
Full-text available
Micro Abstract Crashworthiness optimization problems are characterized by strong nonlinearities and discontinuities. Hence, gradient-based methods cannot be used and alternative approaches have to be considered. Here, a novel, kriging-based method for level set topology optimization is proposed and validated on a crash test case. Compared to CMA-ES...
Conference Paper
In the last decades the replacement of conventional materials with innovative composite ones is increased significantly thanks to their advantages of lightweight and high specific strength. Nevertheless the heterogeneity and the various damage mechanisms of composite materials make their modelling very difficult. Very few attempts have been done fr...

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