Publications related to Use of machine learning for unraveling hidden correlations between particle size distributions and the mechanical behavior of granular materials

Related publications (6)

Bilinear Sequence Regression: A Model for Learning from Long Sequences of High-Dimensional Tokens

Lenka Zdeborová, Emanuele Troiani, Vittorio Erba, Luca Biggio

Current progress in artificial intelligence is centered around so-called large language models that consist of neural networks processing long sequences of high-dimensional vectors called tokens. Statistical physics provides powerful tools to study the fun ...

American Physical Society2025

Intraday solar irradiance forecasting using public cameras

Demetri Psaltis, Mario Paolone, Christophe Moser, Luisa Lambertini, Ilker Oguz, Roy Sarkis

With the significant increase in photovoltaic (PV) electricity generation, more attention has been given to PV power forecasting. Indeed, accurate forecasting allows power grid operators to better schedule and dispatch their assets, such as energy storage ...

Pergamon-Elsevier Science Ltd2024

Complexity of crack front geometry enhances toughness of brittle solids

John Martin Kolinski, Chenzhuo Li, Xinyue Wei

Brittle solids typically fail by growth and propagation of a crack from a surface flaw. This process is modelled using linear elastic fracture mechanics, which parameterizes the toughness of a material by the critical stress intensity factor, or the prefac ...

NATURE PORTFOLIO2024

Backdoor learning curves: explaining backdoor poisoning beyond influence functions

Kathrin Grosse

Backdoor attacks inject poisoning samples during training, with the goal of forcing a machine learning model to output an attacker-chosen class when presented with a specific trigger at test time. Although backdoor attacks have been demonstrated in a varie ...

2024

Explainable ensemble learning model for predicting steel section-concrete bond strength

Xianlin Wang

This study evaluates the efficiency of an explainable ensemble learning framework in precisely predicting the bond strength between steel sections with different surface treatments and various concrete types. Besides seven numerical features, two categoric ...

2022

Traffic Signal Prediction on Transportation Networks Using Spatio-Temporal Correlations on Graphs

Pascal Frossard, Nikolaos Geroliminis, Semin Kwak

Multivariate time series forecasting poses challenges as the variables are intertwined in time and space, like in the case of traffic signals. Defining signals on graphs relaxes such complexities by representing the evolution of signals over a space using ...

2021