Publications associées à Data-driven modeling of beam loss in the LHC

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

EPFL2024

Nanoindentation hardness and modulus of Al2O3-SiO2-CaO and MnO-SiO2-FeO inclusions in iron

Andreas Mortensen, David Hernandez Escobar, Léa Deillon, Alejandra Inés Slagter, Eva Luisa Vogt, Jonathan Aristya Setyadji

Dataset corresponding to the following manuscript: Slagter, A., Setyadji, J.A., Vogt, E.L. et al. Nanoindentation Hardness and Modulus of Al2O3–SiO2–CaO and MnO–SiO2–FeO Inclusions in Iron. Metall Mater Trans A (2024). https://doi.org/10.1007/s11661-024-0 ...

Zenodo2024

Data set for control of Ge island coalescence for the formation of nanowires on silicon

Anna Fontcuberta i Morral, Alok Rudra, Santhanu Panikar Ramanandan, Joel René Sapera, Vladimir Dubrovskii, Sara Marti Sanchez

This document contains all the data and the details of the analysis used in the manuscript titled " Control of Ge island coalescence for the formation of nanowires on silicon." https://doi.org/10.1039/D3NH00573A ...

EPFL Infoscience2024

Hybrid Simulator for Capturing Dynamics of Synthetic Populations

Michel Bierlaire, Marija Kukic

This paper presents a novel hybrid framework for generating and updating a synthetic population. We call it hybrid because it combines model-based and data-driven approaches. Existing generators produce a snapshot of synthetic data that becomes outdated ov ...

IEEE2024

Validation and Application of Hysteresis Loss Model for HTS Stacks and Conductors for Fusion Applications

Nikolay Bykovskiy

Numerous conductor designs for pulsed magnets based on High Temperature Superconductors (HTS), featuring stacks of tapes are currently being proposed. A major contribution to the AC losses is expected to be given by hysteresis losses. Several numerical mod ...

IEEE Institute of Electrical and Electronics Engineers2024

Data and scripts for the RaFSIP scheme

Athanasios Nenes, Paraskevi Georgakaki

This repository contains microphysics routines, scripts, and processed data from the Weather Research and Forecasting (WRF) model simulations presented in the paper "RaFSIP: Parameterizing ice multiplication in models using a machine learning approach", by ...

Zenodo2024

Reducing uncertainties in response predictions of earthquake-damaged masonry buildings using data from image-based inspection

Ian Smith, Katrin Beyer, Bryan German Pantoja Rosero, Mathias Christian Haindl Carvallo

Image information about the state of a building after an earthquake, which can be collected without endangering the post-earthquake reconnaissance activities, can be used to reduce uncertainties in response predictions for future seismic events. This paper ...

Springer2024

Extensions of Peer Prediction Incentive Mechanisms

Adam Julian Richardson

As large, data-driven artificial intelligence models become ubiquitous, guaranteeing high data quality is imperative for constructing models. Crowdsourcing, community sensing, and data filtering have long been the standard approaches to guaranteeing or imp ...

EPFL2024

Dataset for '3D printing of customizable transient bioelectronics and sensors'

Danick Briand, Nicolas Francis Fumeaux

This data set contains the data collected during the FNS project Green Piezo (Grant no. 179064) in association with the recent publication entitled “3D printing of customizable transient bioelectronics and sensors”. This work aims to study and demonstrate ...

Zenodo2024

Spectral Estimators for High-Dimensional Matrix Inference

Farzad Pourkamali

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

EPFL2024