Zobrazeno 1 - 10
of 205
pro vyhledávání: '"Giulio Biroli"'
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-9 (2024)
Abstract We study generative diffusion models in the regime where both the data dimension and the sample size are large, and the score function is trained optimally. Using statistical physics methods, we identify three distinct dynamical regimes duri
Externí odkaz:
https://doaj.org/article/db919330e31f4240b0f565cf222d4db2
Autor:
Rahul N. Chacko, François P. Landes, Giulio Biroli, Olivier Dauchot, Andrea J. Liu, David R. Reichman
Publikováno v:
Physical Review X, Vol 14, Iss 3, p 031012 (2024)
Convincing evidence of domain growth in the heating of ultrastable glasses suggests that the equilibration dynamics of supercooled liquids could be driven by a nucleation and growth mechanism. We investigate this possibility by simulating the equilib
Externí odkaz:
https://doaj.org/article/a2c822efe7074416890391d4ffe34330
Publikováno v:
Machine Learning: Science and Technology, Vol 5, Iss 3, p 035053 (2024)
Normalizing flows can transform a simple prior probability distribution into a more complex target distribution. Here, we evaluate the ability and efficiency of generative machine learning methods to sample the Boltzmann distribution of an atomistic
Externí odkaz:
https://doaj.org/article/8804836f6178484880f4c30c1c5471ff
Publikováno v:
Physical Review X, Vol 13, Iss 4, p 041038 (2023)
We develop a multiscale approach to estimate high-dimensional probability distributions. Our approach applies to cases in which the energy function (or Hamiltonian) is not known from the start. Using data acquired from experiments or simulations we c
Externí odkaz:
https://doaj.org/article/0312d5bbd9c64139b322f0d53270ccb5
Scaling Description of Dynamical Heterogeneity and Avalanches of Relaxation in Glass-Forming Liquids
Publikováno v:
Physical Review X, Vol 13, Iss 3, p 031034 (2023)
We provide a theoretical description of dynamical heterogeneities in glass-forming liquids, based on the premise that relaxation occurs via local rearrangements coupled by elasticity. In our framework, the growth of the dynamical correlation length
Externí odkaz:
https://doaj.org/article/bb8f3058043a4c2abdfa4c2eb4200aa2
Publikováno v:
Physical Review Research, Vol 4, Iss 2, p 023227 (2022)
We use atomistic computer simulations to provide a microscopic description of the brittle failure of amorphous materials, and we assess the role of rare events and quenched disorder. We argue that brittle yielding originates at rare soft regions, sim
Externí odkaz:
https://doaj.org/article/6d18f3fb4f464db29294d8847738f240
Publikováno v:
PLoS Computational Biology, Vol 16, Iss 5, p e1007827 (2020)
When can ecological interactions drive an entire ecosystem into a persistent non-equilibrium state, where many species populations fluctuate without going to extinction? We show that high-diversity spatially heterogeneous systems can exhibit chaotic
Externí odkaz:
https://doaj.org/article/d758669cc6284431af540dd4e7d7e115
Publikováno v:
Physical Review Research, Vol 2, Iss 2, p 023203 (2020)
We numerically study yielding in two-dimensional glasses which are generated with a very wide range of stabilities by swap Monte Carlo simulations and then slowly deformed at zero temperature. We provide strong numerical evidence that stable glasses
Externí odkaz:
https://doaj.org/article/7e9efb821d2440aa9c34edfa2aa2bb73
Autor:
Stefano Sarao Mannelli, Giulio Biroli, Chiara Cammarota, Florent Krzakala, Pierfrancesco Urbani, Lenka Zdeborová
Publikováno v:
Physical Review X, Vol 10, Iss 1, p 011057 (2020)
Gradient-descent-based algorithms and their stochastic versions have widespread applications in machine learning and statistical inference. In this work, we carry out an analytic study of the performance of the algorithm most commonly considered in p
Externí odkaz:
https://doaj.org/article/5be1ae79a5f941e3bce7b8ede2ac53b1
Publikováno v:
Physical Review X, Vol 9, Iss 1, p 011003 (2019)
We study rough high-dimensional landscapes in which an increasingly stronger preference for a given configuration emerges. Such energy landscapes arise in glass physics and inference. In particular, we focus on random Gaussian functions and on the sp
Externí odkaz:
https://doaj.org/article/d6319006785d4e299006e5440ef9b577