1,721,180 research outputs found
Metamaterial filter design via surrogate optimization
Full text linkRecently, an increasing research effort has been dedicated to analyse transmission and dispersion properties of periodic metamaterials containing resonators, and to optimize the amplitude of selected acoustic band gaps between consecutive dispersion curves in the Floquet-Bloch spectrum. Potential novel applications of this research are in the design of passive mechanical filters/diodes. The present work proposes a way to interpolate the objective functions in such band gap optimization problems, using Radial Basis Functions. The study is motivated by the high computational effort often needed for an exact evaluation of the original objective functions, when using iterative optimization algorithms. By replacing such functions with surrogate objective functions, well-performing suboptimal solutions can be obtained with a small computational effort. Numerical results demonstrate the feasibility of the approach
Multi-field asymptotic homogenization approach for Bloch wave propagation in periodic thermodiffusive elastic materials
Full text linkMulti-field asymptotic homogenization methods are proposed to describe the behaviour of periodic Cauchy materials subject to several physical phenomena, focusing on thermodiffusion. The resulting homogenized models provide the overall constitutive tensors and overall inertial terms. Moreover, they allow one to investigate the complex band structures associated with damped Bloch waves travelling in periodic materials, avoiding the challenging computations needed by the adoption of micromechanical approaches
Computational design of innovative mechanical metafilters via adaptive surrogate-based optimization
No full textArchitected materials and metamaterials are a challenging frontier for the development of optimal design strategies targeted at the active and passive control of elastic wave propagation. Within this research field, the microstructural optimization of mechanical metamaterials for achieving desired spectral functionalities may require considerable computational resources. Based on this motivating framework, the present paper illustrates a machine learning methodology to attack the inverse design problem concerning the optimization of the dispersion properties characterizing a novel layered mechanical metamaterial, conceived starting from the bi-tetrachiral periodic topology. Specifically, an adaptive technique is adopted to surrogate and maximize the objective function purposely defined to determine the optimal beam lattice microstructure characterized by the largest stop bandwidth at the lowest centerfrequency (low-cutting mechanical metafilter). The technique is computationally efficient in identifying the existing optimal solution in the physically admissible parameter space. The designed bi-tetrachiral metamaterial provides satisfying broadband low-frequency filtering performances, not achievable by the component tetrachiral layers
Complex frequency band structure of periodic thermo-diffusive materials by Floquet–Bloch theory
No full textThis work deals with the micromechanical study of periodic thermo-diffusive elastic multi-layered materials, which are of interest for the fabrication of solid oxide fuel cells. The focus is on the dynamic regime that is investigating the dispersive wave propagation within the periodic material. In this framework, a generalization of the Floquet–Bloch theory is adopted, able to determine the complex band structure of such materials. The infinite algebraic linear system, obtained by exploiting both bilateral Laplace transform and Fourier transform, is replaced by its finite counterpart, resulting from a proper truncation at a finite number of considered unknowns and equations. A regularization technique is herein useful to get rid of the Gibbs phenomenon. The solution of the problem is, finally, found in terms of complex angular frequencies, corresponding to a finite sequence of eigenvalue problems for given values of the wave vector. The paper is complemented by numerical examples taking into account thermo-mechanical coupling. The frequency band structure of the periodic thermo-diffusive elastic material is found to be strongly influenced by the interaction between thermal and mechanical phenomena
Public Transport Transfers Assessment via Transferable Utility Games and Shapley Value Approximation
No full textThe importance of transfer points in public transport networks is estimated by exploiting an approach based on transferable utility cooperative games, which integrates the network topology and the demands. Transfer points are defined as clusters of nearby stops, from which it is easily possible to switch between routes. The methodology is based on a solution concept from cooperative game theory, known as Shapley value. A special formulation of the game is developed for public transport networks with an emphasis on transfers. Based on such a game, the Shapley value is evaluated as an attribute of each transfer point to measure its relative importance: the greater the associated value, the larger the relevance. Due to the computational requirements of the Shapley value calculation for large-size networks, a Monte Carlo approximation is investigated and adopted. A case study of a real-world network is presented to demonstrate the model’s viability
A Statistical Learning Theory Approach for the Analysis of the Trade-off Between Sample Size and Precision in Truncated Ordinary Least Squares
No full textThis chapter deals with linear regression problems for which one has the possibility of varying the supervision cost per example, by controlling the conditional variance of the output given the feature vector. For a fixed upper bound on the total available supervision cost, the trade-off between the number of training examples and their precision of supervision is investigated, using a nonasymptotic data-independent bound from the literature in statistical learning theory. This bound is related to the truncated output of the ordinary least squares regression algorithm. The results of the analysis are also compared theoretically with the ones obtained in a previous work, based on a large-sample approximation of the untruncated output of ordinary least squares. Advantages and disadvantages of the investigated approach are discussed
A game-theoretic approach for reliability evaluation of public transportation transfers with stochastic features
No full textA game-theoretic approach based on the framework of transferable-utility cooperative games is developed to assess the reliability of transfer nodes in public transportation networks in the case of stochastic transfer times. A cooperative game is defined, whose model takes into account the public transportation system, the travel times, the transfers and the associated stochastic transfer times, and the users’ demand. The transfer stops are modeled as the players of such a game, and the Shapley value – a solution concept in cooperative game theory – is used to identify their centrality and relative importance. Theoretical properties of the model are analyzed. A two-level Monte Carlo approximation of the vector of Shapley values associated with the nodes is introduced, which is efficient and able to take into account the stochastic features of the transportation network. The performance of the algorithm is investigated, together with that of its distributed computing variation. The usefulness of the proposed approach for planners and policy makers is shown with a simple example and on a case study from the public transportation network of Auckland, New Zealand
A Public–Private Insurance Model for Disaster Risk Management: An Application to Italy
Full text linkThis paper proposes a public–private insurance model for earthquakes and floods in Italy in which the insurer and the government co-operate in risk financing. Our model departs from the existing literature by describing an insurance scheme intended to relieve the financial burden that natural events place on governments, while at the same time assisting individuals and protecting the insurance business. Hence, the business aims at maximizing social welfare rather than profits. Given the limited amount of data available on natural risks, expected losses per individual are estimated through risk-modeling. In order to evaluate the insurer’s loss profile, spatial correlation among insured assets is included. Our findings suggest that, when not supported by the government, private insurance might either financially over-expose the insurer or set premiums so high that individuals would fail to purchase policies. This evidence is stronger for earthquake risks, but it is considerable for floods too. We found that jointly managing the two perils alleviates the burden on public capitals by lowering the amount of capitals required and by keeping the probability of additional capital injections into the insurance reserves relatively low
Natural Risk Assessment of Italian Municipalities for Residential Insurance
No full textIn this work, we propose a catastrophe modeling approach to flood and earthquake risk assessment for residential buildings in Italy. This work aims at supporting governors in the definition of a natural risk management strategy. To detect the critical areas of the territory, we compute expected losses per square meter, per municipality, and per structural typology. Our approach allows us to identify the areas where the exposure strongly affects the risk due to the high inhabited density or the presence of fragile buildings. This information is of major relevance for disaster risk reduction. We find that earthquakes in Italy generate annual expected losses approximately equal to 6234.67 million Euros, while flood expected losses amount to about 875.90 million Euros per year. Although earthquakes produce the highest expected losses at the national level, flood losses per square meter often exceed the corresponding earthquake ones
Neural Approximations for Optimal Control and Decision
No full textNeural Approximations for Optimal Control and Decisionprovides a comprehensive methodology
for the approximate solution of functional optimization problems using neural networks and
other nonlinear approximators where the use of traditional optimal control tools is prohibited
by complicating factors like non-Gaussian noise, strong nonlinearities, large dimension of state
and control vectors, etc. Features of the text include: • a general functional optimization
framework; • thorough illustration of recent theoretical insights into the approximate solutions
of complex functional optimization problems; • comparison of classical and neural-network
based methods of approximate solution; • bounds to the errors of approximate solutions; •
solution algorithms for optimal control and decision in deterministic or stochastic environments
with perfect or imperfect state measurements over a finite or infinite time horizon and with one
decision maker or several; • applications of current interest: routing in communications
networks, traffic control, water resource management, etc.;and • numerous, numerically
detailed examples
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