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Institution

Polytechnic University of Milan

EducationMilan, Italy
About: Polytechnic University of Milan is a education organization based out in Milan, Italy. It is known for research contribution in the topics: Finite element method & Population. The organization has 18231 authors who have published 58416 publications receiving 1229711 citations. The organization is also known as: PoliMi & L-NESS.


Papers
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Journal ArticleDOI
TL;DR: In this article, the energy and geometrical features of the interaction are described along with the atomic characteristics that confer molecules with the specific ability to interact through this interaction, and some principles are presented for crystal engineering based on halogen-bonding interactions.
Abstract: Halogen bonding is the noncovalent interaction where halogen atoms function as electrophilic species. The energetic and geometrical features of the interaction are described along with the atomic characteristics that confer molecules with the specific ability to interact through this interaction. Halogen bonding has an impact on all research fields where the control of intermolecular recognition and self-assembly processes plays a key role. Some principles are presented for crystal engineering based on halogen-bonding interactions. The potential of the interaction is also shown by applications in liquid crystals, magnetic and conducting materials, and biological systems.

1,358 citations

Journal ArticleDOI
TL;DR: The results show that a cellular, tissue-engineered airway with mechanical properties that allow normal functioning, and which is free from the risks of rejection, is produced, suggesting that autologous cells combined with appropriate biomaterials might provide successful treatment for patients with serious clinical disorders.

1,355 citations

Proceedings ArticleDOI
TL;DR: In this paper, the authors presented results obtained using 45 ERS SAR images gathered over the Italian town of Camaiore (within a time span of more than 6 years and a range of normal baseline of over 2000 m) are presented.
Abstract: Differential SAR interferometry measurements provide a unique tool for low-cost, large-coverage surface deformations monitoring. Limitations are essentially due to temporal decorrelation and atmospheric inhomogeneities. Though temporal decorrelation and atmospheric disturbances strongly affect interferogram quality, reliable deformation measurements can be obtained in a multi-image framework on a small subset of image pixels, corresponding to stable areas. These points, hereafter called Permanent Scatterers, can be used as a `natural GPS network' to monitor terrain motion, analyzing the phase history of each one. In this paper, results obtained using 45 ERS SAR images gathered over the Italian town of Camaiore (within a time span of more than 6 years and a range of normal baseline of more than 2000 m) are presented. The area is of high geophysical interest because it is known to be unstable. A subterranean cavity collapsed in October 1995 causing the ruin of several houses in that location. Time series analysis of the phase values showed the presence of precursors three months before the collapse.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

1,320 citations

Journal ArticleDOI
TL;DR: This work demonstrates algebraic convergence with respect to the total number of collocation points and quantifies the effect of the dimension of the problem (number of input random variables) in the final estimates, indicating for which problems the sparse grid stochastic collocation method is more efficient than Monte Carlo.
Abstract: This work proposes and analyzes a Smolyak-type sparse grid stochastic collocation method for the approximation of statistical quantities related to the solution of partial differential equations with random coefficients and forcing terms (input data of the model). To compute solution statistics, the sparse grid stochastic collocation method uses approximate solutions, produced here by finite elements, corresponding to a deterministic set of points in the random input space. This naturally requires solving uncoupled deterministic problems as in the Monte Carlo method. If the number of random variables needed to describe the input data is moderately large, full tensor product spaces are computationally expensive to use due to the curse of dimensionality. In this case the sparse grid approach is still expected to be competitive with the classical Monte Carlo method. Therefore, it is of major practical relevance to understand in which situations the sparse grid stochastic collocation method is more efficient than Monte Carlo. This work provides error estimates for the fully discrete solution using $L^q$ norms and analyzes the computational efficiency of the proposed method. In particular, it demonstrates algebraic convergence with respect to the total number of collocation points and quantifies the effect of the dimension of the problem (number of input random variables) in the final estimates. The derived estimates are then used to compare the method with Monte Carlo, indicating for which problems the former is more efficient than the latter. Computational evidence complements the present theory and shows the effectiveness of the sparse grid stochastic collocation method compared to full tensor and Monte Carlo approaches.

1,257 citations


Authors

Showing all 18743 results

NameH-indexPapersCitations
Alex J. Barker132127384746
Pierluigi Zotto128119778259
Andrea C. Ferrari126636124533
Marco Dorigo10565791418
Marcello Giroletti10355841565
Luciano Gattinoni10361048055
Luca Benini101145347862
Alberto Sangiovanni-Vincentelli9993445201
Surendra P. Shah9971032832
X. Sunney Xie9822544104
Peter Nijkamp97240750826
Nicola Neri92112241986
Ursula Keller9293433229
A. Rizzi9165340038
Martin J. Blunt8948529225
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023302
2022811
20214,151
20204,301
20193,831
20183,767