Publications related to Wear across scales

Related publications (12)

Automated Parameterization for Aerodynamic Shape Optimization via Deep Geometric Learning

Traditional aerodynamic shape optimization (ASO) is costly and time-intensive, requiring extensive manual tuning for parameterization to define the design space and manipulate geometries. Conventional parameterization methods limit flexibility and demand s ...

American Institute of Aeronautics and Astronautics (AIAA)2025

Fixed point geometry in chaotic neural networks

Christian Keup

Understanding the high-dimensional chaotic dynamics occurring in complex biological systems such as recurrent neural networks or ecosystems remains a conceptual challenge. For low-dimensional dynamics, fixed points provide the geometric scaffold of the dyn ...

American Physical Society (APS)2025

Expanding density-correlation machine learning representations for anisotropic coarse-grained particles

Jigyasa Nigam, Kevin Kazuki Huguenin-Dumittan

Physics-based, atom-centered machine learning (ML) representations have been instrumental to the effective integration of ML within the atomistic simulation community. Many of these representations build off the idea of atoms as having spherical, or isotro ...

2024

Towards Trustworthy Deep Learning for Image Reconstruction

Alexis Marie Frederic Goujon

The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

EPFL2024

GEMTELLIGENCE: Accelerating gemstone classification with deep learning

Olga Fink

The value of luxury goods, particularly investment-grade gemstones, is influenced by their origin and authenticity, often resulting in differences worth millions of dollars. Traditional methods for determining gemstone origin and detecting treatments invol ...

2024

Predicting protein interactions using geometric deep learning on protein surfaces

Freyr Sverrisson

In the domain of computational structural biology, predicting protein interactions based on molecular structure remains a pivotal challenge. This thesis delves into this challenge through a series of interconnected studies. The first chapter introduces th ...

EPFL2024

Turbulence and Heat exchange in the Near-Surface Boundary Layer over Patchy Snow

Michael Haugeneder

The atmospheric layer adjacent to the earth's surface is of crucial importance for weather models due to the exchange of energy between the surface and the atmosphere. This exchange is dependent on the various surface properties and influences the state of ...

EPFL2024

Combining Weather Station Data and Short-Term LiDAR Deployment to Estimate Wind Energy Potential with Machine Learning: A Case Study from the Swiss Alps

Michael Lehning, Wolf Hendrik Huwald, Jérôme François Sylvain Dujardin, Franziska Gerber, Fanny Kristianti

Wind energy potential in complex terrain is still poorly understood and difficult to quantify. With Switzerland’s current efforts to shift to renewable energy resources, it is now becoming even more crucial to investigate the hidden potential of wind energ ...

2023

An end-to-end pipeline for historical censuses processing

Rémi Guillaume Petitpierre, Lucas Arnaud André Rappo, Marion Kramer

Censuses are structured documents of great value for social and demographic history, which became widespread from the nineteenth century on. However, the plurality of formats and the natural variability of historical data make their extraction arduous and ...

2023

Neural and algorithmic bases of odor guided trail following in mice

Alexander Mathis

Oxford Univ Press2023

Revisiting Evaluation Metrics for Semantic Segmentation: Optimization and Evaluation of Fine-grained Intersection over Union

Devis Tuia

Semantic segmentation datasets often exhibit two types of imbalance: \textit{class imbalance}, where some classes appear more frequently than others and \textit{size imbalance}, where some objects occupy more pixels than others. This causes traditional eva ...

2023

DeePMD-kit v2: A software package for deep potential models

Davide Tisi

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of phy ...

AIP Publishing2023