Publication

Sparse approximation of multilinear problems with applications to kernel-based methods in UQ

Publications associées (31)

Density estimation in RKHS with application to Korobov spaces in high dimensions

A kernel method for estimating a probability density function from an independent and identically distributed sample drawn from such density is presented. Our estimator is a linear combination of kernel functions, the coefficients of which are determined b ...

2023

Learning ground states of gapped quantum Hamiltonians with Kernel Methods

Giuseppe Carleo, Filippo Vicentini, Riccardo Rossi, Clemens Giuliani

Neural network approaches to approximate the ground state of quantum hamiltonians require the numerical solution of a highly nonlinear optimization problem. We introduce a statistical learning approach that makes the optimization trivial by using kernel me ...

Verein Forderung Open Access Publizierens Quantenwissenschaf2023

Uncertainty-aware Flexibility Envelope Prediction in Buildings with Controller-agnostic Battery Models

Baptiste Schubnel, Colin Neil Jones, Rafael Eduardo Carrillo Rangel, Pierre-Jean Alet, Paul Scharnhorst

Buildings are a promising source of flexibility for the application of demand response. In this work, we introduce a novel battery model formulation to capture the state evolution of a single building. Being fully data-driven, the battery model identificat ...

IEEE2023

On double-descent in uncertainty quantification in overparametrized models

Florent Gérard Krzakala, Lenka Zdeborová, Bruno Loureiro, Lucas Andry Clarte, Bruno Loureiro

Uncertainty quantification is a central challenge in reliable and trustworthy machine learning. Naive measures such as last-layer scores are well-known to yield overconfident estimates in the context of overparametrized neural networks. Several methods, ra ...

PMLR Proceedings of Machine Learning Research2023

Reducing Aerosol Forcing Uncertainty by Combining Models With Satellite and Within-The-Atmosphere Observations: A Three-Way Street

Athanasios Nenes

Aerosol forcing uncertainty represents the largest climate forcing uncertainty overall. Its magnitude has remained virtually undiminished over the past 20 years despite considerable advances in understanding most of the key contributing elements. Recent wo ...

AMER GEOPHYSICAL UNION2023

Efficient approximation of cardiac mechanics through reduced-order modeling with deep learning-based operator approximation

Alfio Quarteroni, Andrea Manzoni

Reducing the computational time required by high-fidelity, full-order models (FOMs) for the solution of problems in cardiac mechanics is crucial to allow the translation of patient-specific simulations into clinical practice. Indeed, while FOMs, such as th ...

Wiley2023

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Mehmet Fatih Sahin

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

EPFL2023

lifex-ep: a robust and efficient software for cardiac electrophysiology simulations

Alfio Quarteroni, Francesco Regazzoni, Marco Fedele, Stefano Pagani

Background: Simulating the cardiac function requires the numerical solution of multi-physics and multi-scale mathematical models. This underscores the need for streamlined, accurate, and high-performance computational tools. Despite the dedicated endeavors ...

BMC2023

Euclid preparation XXIV. Calibration of the halo mass function in λ(ν)CDM cosmologies

Slobodan Ilic, Georges Meylan, Frédéric Courbin, Fabio Finelli, Maurizio Martinelli, Gianluca Castignani, Marcello Farina, Austin Chandler Peel, Yi Wang

Euclid's photometric galaxy cluster survey has the potential to be a very competitive cosmological probe. The main cosmological probe with observations of clusters is their number count, within which the halo mass function (HMF) is a key theoretical quanti ...

EDP SCIENCES S A2023

Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial

Olga Fink, Luca Biggio

On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an essential layer of safety assurance that could lead to more principled decision making by enabling sound risk assessment and management. The safety and reliability impr ...

2023

Inferring free-energy barriers and kinetic rates from molecular dynamics via underdamped Langevin models

Fabio Pietrucci, Sara Bonella, David Daniel Girardier

Rare events include many of the most interesting transformation processes in condensed matter, from phase transitions to biomolecular conformational changes to chemical reactions. Access to the corresponding mechanisms, free-energy landscapes and kinetic r ...

Aip Publishing2023