Publications related to Approximation in the extended functional tensor train format

Related publications (7)

Integer traffic assignment problem: Algorithms and insights on random graphs

Rayan Harfouche, Lenka Zdeborová, Giovanni Piccioli

Path optimization is a fundamental concern across various real-world scenarios, ranging from traffic congestion issues to efficient data routing over the Internet. The traffic assignment problem (TAP) is a classic continuous optimization problem in this fi ...

2025

Structural equation models for multivariate extremes

Mario Krali

This thesis contributes to the modelling of multivariate rare events by combining extreme value theory and graphical modelling. The central theme involves structural equation models (SEMs), which are powerful tools used to express causal extremal dependenc ...

EPFL2025

An Efficient Machine Learning Model for Lightning Localization via Lightning-Induced Voltages on Transmission Lines

Farhad Rachidi-Haeri, Marcos Rubinstein, Hamidreza Karami

In this paper, three machine learning (ML)-based approaches—XGBoost, Artificial Neural Network (ANN), and Random Forest algorithms—are compared for the localization of lightning through the analysis of lightninginduced voltages on power transmission lines. ...

URSI – International Union of Radio Science2024

Machine learning-aided generative molecular design

Philippe Schwaller, Jeff Guo

Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...

Nature Portfolio2024

A multi-objective planning method for multi-energy complementary distributed energy system: Tackling thermal integration and process synergy

François Maréchal, Hangyu Yu

This study proposes a multi-objective optimization methodology for planning multi-energy complementary distributed energy systems considering process synergy and thermal integration. The process integration tech-nique is integrated into the Energy Hub mode ...

ELSEVIER SCI LTD2023

A Lagrangian decomposition scheme for choice-based optimization

Michel Bierlaire, Meritxell Pacheco Paneque

Choice-based optimization problems are the family of optimization problems that incorporate the stochasticity of individual preferences according to discrete choice models to make planning decisions. This integration brings non-convexity and nonlinearity t ...

PERGAMON-ELSEVIER SCIENCE LTD2022

Time-Optimal Path-Following Operation in the Presence of Uncertainty

Dominique Bonvin, Timm Faulwasser, Alejandro Gabriel Marchetti, Predrag Milosavljevic

Path-following tasks, which refer to dynamic motion planning along pre-specified geometric references, are frequently encountered in applications such as milling, robot-supported measurements, and trajectory planning for autonomous vehicles. Different conv ...

Ieee2016