Zobrazeno 1 - 10
of 489
pro vyhledávání: '"Mainini, P."'
The paper is devoted to the linearization of the non linear Signorini functional in the incompressible case. The limit functional, in the sense of Gamma-convergence, may coincide with the expected one only in some particular cases.
Externí odkaz:
http://arxiv.org/abs/2407.20137
We exhibit existence of non-trivial solutions of some fractional linear Schr\"odinger equations which are radial and vanish at the origin. This is in stark contrast to what happens in the local case. We also prove analogous results in the presence of
Externí odkaz:
http://arxiv.org/abs/2407.04414
This article extends, in a stochastic setting, previous results in the determination of feasible exchange ratios for merging companies. A first outcome is that shareholders of the companies involved in the merging process face both an upper and a low
Externí odkaz:
http://arxiv.org/abs/2401.02681
Autor:
Kružík, Martin, Mainini, Edoardo
We propose models in nonlinear elasticity for nonsimple materials that include surface energy terms. Additionally, we also discuss living surface loads on the boundary. We establish corresponding linearized models and show their relationship to the o
Externí odkaz:
http://arxiv.org/abs/2312.08783
Autor:
Di Fiore, Francesco, Mainini, Laura
Publikováno v:
Computers & Structures 296 (2024): 107302
The adoption of high-fidelity models for many-query optimization problems is majorly limited by the significant computational cost required for their evaluation at every query. Multifidelity Bayesian methods (MFBO) allow to include costly high-fideli
Externí odkaz:
http://arxiv.org/abs/2312.05831
Autor:
Mainini, Edoardo, Percivale, Danilo
We show convergence of minimizers of weighted inertia-energy functionals to solutions of initial value problems for a class of nonlinear wave equations. The result is given for the nonhomogeneous case under a natural growth assumption on the forcing
Externí odkaz:
http://arxiv.org/abs/2305.00731
Autor:
Mainini, Edoardo, Percivale, Danilo
Publikováno v:
Advances in Continuous and Discrete Models (2023), 20:2023
We show that the solution of the Cauchy problem for the classical ode $m \mathbf y''=\mathbf f$ can be obtained as limit of minimizers of exponentially weighted convex variational integrals. This complements the known results about weighted inertia-e
Externí odkaz:
http://arxiv.org/abs/2304.15007
Publikováno v:
Archives of Computational Methods in Engineering (2024): 1-29
Science and Engineering applications are typically associated with expensive optimization problems to identify optimal design solutions and states of the system of interest. Bayesian optimization and active learning compute surrogate models through e
Externí odkaz:
http://arxiv.org/abs/2303.01560
Autor:
Marta Sevieri, Francesco Andreata, Francesco Mainini, Lorena Signati, Francesca Piccotti, Marta Truffi, Arianna Bonizzi, Leopoldo Sitia, Claudia Pigliacelli, Carlo Morasso, Barbara Tagliaferri, Fabio Corsi, Serena Mazzucchelli
Publikováno v:
Journal of Nanobiotechnology, Vol 22, Iss 1, Pp 1-16 (2024)
Abstract Despite the advent of numerous targeted therapies in clinical practice, anthracyclines, including doxorubicin (DOX), continue to play a pivotal role in breast cancer (BC) treatment. DOX directly disrupts DNA replication, demonstrating remark
Externí odkaz:
https://doaj.org/article/3c3648d1ce4b4122a7947ebaaeebe0d2
Autor:
Di Fiore, Francesco, Mainini, Laura
Publikováno v:
Knowledge-Based Systems 299 (2024): 111959
Bayesian optimization is a popular framework for the optimization of black box functions. Multifidelity methods allows to accelerate Bayesian optimization by exploiting low-fidelity representations of expensive objective functions. Popular multifidel
Externí odkaz:
http://arxiv.org/abs/2207.06325