Publications related to On evaluation of thermophysical properties of transformer oil-based nanofluids: A comprehensive modeling and experimental study

Related publications (34)

Targeted Conversion of Biomass into Primary Diamines via Carbon Shell-Confined Cobalt Nanoparticles

Primary diamines are valuable yet challenging to synthesize due to issues such as product and intermediate condensation and catalyst poisoning. To address these problems, effective synthesis systems must be explored. Here, 2,5-bis(aminomethyl)furan (BAMF), ...

AMER CHEMICAL SOC2025

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Alexander Mathis, Alberto Silvio Chiappa, Alessandro Marin Vargas, Axel Bisi

Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

EPFL Infoscience2024

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Scott William Pesme

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

EPFL2024

A setup for hard x-ray time-resolved resonant inelastic x-ray scattering at SwissFEL

Majed Chergui, Giulia Fulvia Mancini, Michele Puppin, Ludmila Maria Diniz Leroy, Hui-Yuan Chen, Rolf Baldwin Versteeg

We present a new setup for resonant inelastic hard x-ray scattering at the Bernina beamline of SwissFEL with energy, momentum, and temporal resolution. The compact R = 0.5 m Johann-type spectrometer can be equipped with up to three crystal analyzers and al ...

Aip Publishing2024

Safe Deep Neural Networks

Kyle Michael Matoba

The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is troubli ...

EPFL2024

Innovative thermophysical characterization of an ATF composite cladding: the case of silicon carbide

Andrea Cavaliere

Nuclear power is a powerful technology that plays an important role in the fight against climate change, and research is continuously engaged in studies that could further improve its safety. After the Fukushima accident, Accident Tolerant Fuels research h ...

EPFL2024

Gibbs sampling the posterior of neural networks

Lenka Zdeborová, Emanuele Troiani, Giovanni Piccioli

In this paper, we study sampling from a posterior derived from a neural network. We propose a new probabilistic model consisting of adding noise at every pre- and post-activation in the network, arguing that the resulting posterior can be sampled using an ...

Iop Publishing Ltd2024

On the number of regions of piecewise linear neural networks

Michaël Unser, Alexis Marie Frederic Goujon

Many feedforward neural networks (NNs) generate continuous and piecewise-linear (CPWL) mappings. Specifically, they partition the input domain into regions on which the mapping is affine. The number of these so-called linear regions offers a natural metric ...

2024

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Mattia Atzeni

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

EPFL2024

Machine Learning for Modeling Stock Returns

Teng Andrea Xu

Throughout history, the pace of knowledge and information sharing has evolved into an unthinkable speed and media. At the end of the XVII century, in Europe, the ideas that would shape the "Age of Enlightenment" were slowly being developed in coffeehouses, ...

EPFL2024

Unearthing the foundational role of anharmonicity in heat transport in glasses

Paolo Pegolo

The time-honored Allen -Feldman theory of heat transport in glasses is generally assumed to predict a finite value for the thermal conductivity, even if it neglects the anharmonic broadening of vibrational normal modes. We demonstrate that the harmonic app ...

Amer Physical Soc2024

Thermal conductivity of Li 3 PS 4 solid electrolytes with ab initio accuracy

Michele Ceriotti, Federico Grasselli, Davide Tisi, Lorenzo Gigli

The vast amount of computational studies on electrical conduction in solid-state electrolytes is not mirrored by comparable efforts addressing thermal conduction, which has been scarcely investigated despite its relevance to thermal management and (over)he ...

Amer Physical Soc2024

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Romain Christophe Rémy Fleury, Janez Rus

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

2024

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Volkan Cevher, Grigorios Chrysos, Fanghui Liu

Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate scaled ResNet in the limit of infinitely deep and wide neural networks, of wh ...

2024

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Alexander Carl Unnervik

The field of biometrics, and especially face recognition, has seen a wide-spread adoption the last few years, from access control on personal devices such as phones and laptops, to automated border controls such as in airports. The stakes are increasingly ...

EPFL2024

Topics in statistical physics of high-dimensional machine learning

Hugo Chao Cui

In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

EPFL2024

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Pakshal Narendra Bohra

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

EPFL2024

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Alexander Mathis, Alberto Silvio Chiappa, Alessandro Marin Vargas, Axel Bisi

Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...

Zenodo2024

Performance of energy piles foundation in hot-dominated climate: A case study in Dubai

Lyesse Laloui, Elena Ravera, Sofie Elaine ten Bosch

Energy piles represent an innovative technology that can help provide sustainable geothermal heating or cooling energy for thermal conditioning purposes. In hot-dominated climates, the interest is to inject heat in the ground and extract energy for space-c ...

Pergamon-Elsevier Science Ltd2024

Decomposition method to evaluate district heating/cooling network potential at urban scale

Ana Catarina Gouveia Braz, François Maréchal, Luc Girardin, Bingqian Liu

In this work, a tool to design district heating networks (DHN) is presented and applied to the city of Lausanne as a case study. The evaluation of the buildings’ heat/cooling demand is performed using a Geographic Information System (GIS) database, built f ...

Associazione Italiana Di Ingegneria Chimica2024