Publications associées à Neoclassical equilibria as starting point for global gyrokinetic microturbulence simulations

Publications associées (34)

Global existence for perturbations of the 2D stochastic Navier-Stokes equations with space-time white noise

We prove global in time well-posedness for perturbations of the 2D stochastic Navier-Stokes equations partial derivative( t)u + u center dot del u = Delta u - del p + sigma + xi, u(0, center dot ) = u(0),div (u) = 0, driven by additive space-time white noi ...

Springernature2024

Exploiting the Signal-Leak Bias in Diffusion Models

Athanasios Fitsios, Sabine Süsstrunk, Radhakrishna Achanta, Mahmut Sami Arpa, Martin Nicolas Everaert

There is a bias in the inference pipeline of most diffusion models. This bias arises from a signal leak whose distribution deviates from the noise distribution, creating a discrepancy between training and inference processes. We demonstrate that this signa ...

2024

Iterative removal of sources to model the turbulent electromotive force

Salim Bennouna, Abhijit Bhausaheb Bendre

We describe a novel method to compute the components of dynamo tensors from direct magnetohydrodynamic (MHD) simulations. Our method relies upon an extension and generalization of the standard H & ouml;gbom CLEAN algorithm widely used in radio astronomy to ...

Oxford Univ Press2024

Combining electric and magnetic responses with hybrid nanophotonic systems

Olivier Martin

Over the last decade, metasurfaces have proven extremely useful and versatile to design a broad range of photonic systems in a very innovative way. However, most optical metasurfaces demonstrated so far rely on meta-atoms that are either in plasmonic metal ...

2024

Magnetism of Single Surface Adsorbed Atoms Studied with Radio-Frequency STM

Clément Marie Soulard

This thesis investigates the magnetic properties of single atoms and molecules adsorbed on thin magnesium oxide decoupling layers, grown on a silver single crystal. To address these systems experimentally, we use a low temperature scanning tunneling micros ...

EPFL2024

Functional data analysis with rough sample paths?

Victor Panaretos, Neda Mohammadi Jouzdani

Functional data are typically modeled as sample paths of smooth stochastic processes in order to mitigate the fact that they are often observed discretely and noisily, occasionally irregularly and sparsely. The smoothness assumption is imposed to allow for ...

TAYLOR & FRANCIS LTD2024

Model-based constraints for trajectory determination of quad-copters: Design, calibration & merits for direct orientation

Jan Skaloud, Davide Antonio Cucci, Kenneth Joseph Paul

This paper proposes a novel method to improve georeferencing of airborne laser scanning by improved trajectory estimation using Vehicle Dynamic Model. In Vehicle Dynamic Model (VDM), the relationship between the dynamics of the platform and control inputs ...

2023

Optimal digital filter selection for remote photoplethysmography (rPPG) signal conditioning

Ata Jedari Golparvar

Remote photoplethysmography (rPPG) using camera-based imaging has shown excellent potential recently in vital signs monitoring due to its contactless nature. However, the optimum filter selection for pre-processing rPPG data in signal conditioning is still ...

IOP Publishing Ltd2023

Disentangling 1/f noise from confined ion dynamics

Théo Denis Tobias Emmerich

Ion transport through biological and solid-state nanochannels is known to be a highly noisy process. The power spectrum of current fluctuations is empirically known to scale like the inverse of frequency, following the long-standing yet poorly understood H ...

ROYAL SOC CHEMISTRY2023

WINC: A Wireless IoT Network for Multi-Noise Source Cancellation

Haitham Al Hassanieh, Junfeng Guan

This paper introduces Wireless IoT-based Noise Cancellation (WINC) which defines a framework for leveraging a wireless network of IoT microphones to enhance active noise cancellation in noise-canceling headphones. The IoT microphones forward ambient noise ...

Assoc Computing Machinery2023

Universal and adaptive methods for robust stochastic optimization

Ali Kavis

Within the context of contemporary machine learning problems, efficiency of optimization process depends on the properties of the model and the nature of the data available, which poses a significant problem as the complexity of either increases ad infinit ...

EPFL2023

Multi-agent Learning with Privacy Guarantees

Elsa Rizk

A multi-agent system consists of a collection of decision-making or learning agents subjected to streaming observations from some real-world phenomenon. The goal of the system is to solve some global learning or optimization problem in a distributed or dec ...

EPFL2023

Predictive modeling of Alfvén eigenmode stability in inductive scenarios in JT-60SA

Javier García Hernández, Antonio José Pereira de Figueiredo, Matteo Vallar

The JT-60SA device offers unique conditions before ITER for the study of the interaction of energetic particles with plasma waves. With similar dimensions to JET, e.g., a major radius but with a slightly more elongated plasma volume, JT-60SA is used as a h ...

Frontiers Media Sa2023

Housing, street and health: a new systemic research framework

Paola Viganò, Stéphane Joost, Dusan Licina, Idris Guessous, Derek Pierre Christie, Anna Pagani, Valentin Daniel Maurice Bourdon, Mathias Lerch, Catarina Wall Gago

As the world’s population grows in cities, urban dwellers spend a large amount of time inside their home, making housing health ever more important. Critical for residents’ health, the interactions between indoor residential environments and outdoor enviro ...

2023

Data for: "A Neural-Network-Based Convex Regularizer for Inverse Problems"

Michaël Unser, Alexis Marie Frederic Goujon, Pakshal Narendra Bohra, Sebastian Jonas Neumayer, Stanislas Ducotterd

Data for: "A Neural-Network-Based Convex Regularizer for Inverse Problems". The corresponding scripts can be accessed on GitHub (https://github.com/axgoujon/convex_ridge_regularizers). The data is organized as follows: - ct_data_sets.tar.gz: contains prepr ...

Zenodo2023

Universal machine learning for the response of atomistic systems to external fields

Yaolong Zhang

Machine learned interatomic interaction potentials have enabled efficient and accurate molecular simulations of closed systems. However, external fields, which can greatly change the chemical structure and/or reactivity, have been seldom included in curren ...

Nature Portfolio2023

Improving algal bloom detection using spectroscopic analysis and machine learning: A case study in a large artificial reservoir, South Korea

Thanh Trung Huynh

The prediction of algal blooms using traditional water quality indicators is expensive, labor-intensive, and timeconsuming, making it challenging to meet the critical requirement of timely monitoring for prompt management. Using optical measures for foreca ...

ELSEVIER2023

Direct observation of exchange anisotropy in the helimagnetic insulator Cu2OSeO3

Arnaud Magrez, Jonathan White, Priya Ranjan Baral

The helical magnetic structures of cubic chiral systems are well explained by the competition among Heisen-berg exchange, Dzyaloshinskii-Moriya interaction, cubic anisotropy, and anisotropic exchange interaction (AEI). Recently, the role of the latter has ...

AMER PHYSICAL SOC2023

Possible restoration of particle-hole symmetry in the 5/2-quantized Hall state at small magnetic field

Frédéric Mila, Loïc Jean Pierre Herviou

Motivated by the experimental observation of a quantized 5/2 thermal conductance at filling nu = 5/2, a result incompatible with both the Pfaffian and the anti-Pfaffian states, we have pushed the expansion of the effective Hamiltonian of the 5/2-quantized ...

AMER PHYSICAL SOC2023

Distributionally Robust Linear Quadratic Control

Daniel Kuhn, Bahar Taskesen, Cagil Kocyigit

Linear-Quadratic-Gaussian (LQG) control is a fundamental control paradigm that is studied in various fields such as engineering, computer science, economics, and neuroscience. It involves controlling a system with linear dynamics and imperfect observations ...

2023