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Monitoring of the Thermal Strain Distribution in CICCs During the Cyclic Loading Tests in SULTAN

Related publications (136)

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Tree root distribution modelling in different environmental conditions

David Andrew Barry, Paolo Perona, Massimiliano Schwarz

The ability to predict the spatial distribution of tree root system variables (e.g., the Root system Area (RA), the maximum root diameter, the number of roots in diameter classes, the density of fine roots, etc.) under different environmental conditions is ...

2022

Comparing Annual Glare Simulation Methods

Stephen William Wasilewski

Recently, there have been multiple proposals for faster methods to calculate glare metrics, daylight glare probability (DGP) in particular. This is driven simultaneously by the lengthy times required to simulate DGP with a conventional image-based approach ...

2022

Unbiased Monte Carlo cluster updates with autoregressive neural networks

Giuseppe Carleo, Riccardo Rossi, Dian Wu

Efficient sampling of complex high-dimensional probability distributions is a central task in computational science. Machine learning methods like autoregressive neural networks, used with Markov chain Monte Carlo sampling, provide good approximations to s ...

AMER PHYSICAL SOC2021

Influence of residual stresses on the buckling capacity of axially loaded steel columns

Gabriele Falconi

Over the past 60-80 years, the design methodology of steel structures comprising conventional steel profiles, such as HEA, HEB or HEM, has remained unchanged. However, during the 21st century, production methods have improved considerably. The aim of this ...

2021

In multiple testing problems where the components come from a mixture model of noise and true effect, we seek to first test for the existence of the non-zero components, and then identify the true alternatives under a fixed significance level

\alpha

. Two ...

EPFL2021

A universal signature in the melting of metallic nanoparticles

Kevin Rossi

Predicting when phase changes occur in nanoparticles is fundamental for designing the next generation of devices suitable for catalysis, biomedicine, optics, chemical sensing and electronic circuits. The estimate of the temperature at which metallic nanopa ...

ROYAL SOC CHEMISTRY2021

Training datasets for machine learning often have some form of missingness. For example, to learn a model for deciding whom to give a loan, the available training data includes individuals who were given a loan in the past, but not those who were not. This ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2021

Temperature-Dependent Electronic Ground-State Charge Transfer in van der Waals Heterostructures

Haiyuan Wang, Mariana Rossi Carvalho, Dmitrii Maksimov

Electronic charge rearrangement between components of a heterostructure is the fundamental principle to reach the electronic ground state. It is acknowledged that the density of state distribution of the components governs the amount of charge transfer, bu ...

2021

The spectral distribution plays a key role in the statistical modelling of multivariate extremes, as it defines the dependence structure of multivariate extreme-value distributions and characterizes the limiting distribution of the relative sizes of the co ...

EPFL2020

We consider three classes of linear differential equations on distribution functions, with a fractional order alpha is an element of [0; 1]. The integer case alpha = 1 corresponds to the three classical extreme families. In general, we show that there is a ...

2020

\alpha

. Two ...

EPFL2021