Publications associées à Stability Analysis of a Partitioned Fluid-Structure Thermal Coupling Algorithm

Publications associées (35)

The Cost of Consistency: Submodular Maximization with Constant Recourse

In this work, we study online submodular maximization and how the requirement of maintaining a stable solution impacts the approximation. In particular, we seek bounds on the best-possible approximation ratio that is attainable when the algorithm is allowe ...

ACM2025

Reach For the Arcs: Reconstructing Surfaces from SDFs via Tangent Points

Yingying Ren

We introduce an algorithm to reconstruct a mesh from discrete samples of a shape's Signed Distance Function (SDF). A simple geometric reinterpretation of the SDF lets us formulate the problem through a point cloud, from which a surface can be extracted wit ...

Association for Computing Machinery2024

A ride time-oriented scheduling algorithm for dial-a-ride problems

Nikolaos Geroliminis, Claudia Bongiovanni, Mor Kaspi

This paper offers a new algorithm to efficiently optimize scheduling decisions for dial-a-ride problems (DARPs), including problem variants considering electric and autonomous vehicles (e-ADARPs). The scheduling heuristic, based on linear programming theor ...

Pergamon-Elsevier Science Ltd2024

Energy-Aware Decentralized Learning with Intermittent Model Training

Anne-Marie Kermarrec, Rafael Pereira Pires, Rishi Sharma, Marinus Abraham de Vos, Akash Balasaheb Dhasade

SKIPTRAIN is a novel Decentralized Learning (DL) algorithm, which minimizes energy consumption in decentralized learning by strategically skipping some training rounds and substituting them with synchronization rounds. These trainingsilent periods, besides ...

IEEE2024

On the conjugate symmetry and sparsity of the harmonic decomposition of parametric surfaces with the randomised Kaczmarz method

Katrin Beyer

This limitation is circumvented by using scalable and non–memory–intensive harmonic expansions. In this paper, the projection of parametric surfaces onto disk and spherical harmonics bases is investigated, and three novel computationally efficient algorith ...

2024

Adaptive projected variational quantum dynamics

Giuseppe Carleo, Stefano Barison, David Linteau

We propose an adaptive quantum algorithm to prepare accurate variational time evolved wave functions. The method is based on the projected variational quantum dynamics (pVQD) algorithm, that performs a global optimization with linear scaling in the number ...

Amer Physical Soc2024

Gibbs sampling the posterior of neural networks

Lenka Zdeborová, Emanuele Troiani, Giovanni Piccioli

In this paper, we study sampling from a posterior derived from a neural network. We propose a new probabilistic model consisting of adding noise at every pre- and post-activation in the network, arguing that the resulting posterior can be sampled using an ...

Iop Publishing Ltd2024

Anomalous dissipation and other non-smooth phenomena in fluids

Massimo Sorella

The thesis is dedicated to the study of two main partial differential equations (PDEs) in fluid dynamics: the Navier-Stokes equations, which describe the motion of incompressible fluids, and the transport equation with divergence-free velocity fields, whic ...

EPFL2024

Energy Management of Price-Maker Community Energy Storage by Stochastic Dynamic Programming

Xue Zhang, Tianshu Yang

In this paper, we propose an analytical stochastic dynamic programming (SDP) algorithm to address the optimal management problem of price-maker community energy storage. As a price-maker, energy storage smooths price differences, thus decreasing energy arb ...

China Electric Power Research Inst2024

Molecular hypergraph neural networks

Philippe Schwaller, Junwu Chen

Graph neural networks (GNNs) have demonstrated promising performance across various chemistry-related tasks. However, conventional graphs only model the pairwise connectivity in molecules, failing to adequately represent higher order connections, such as m ...

Aip Publishing2024

Bayesian reconstruction of memories stored in neural networks from their connectivity

Lenka Zdeborová, Florent Gérard Krzakala

The advent of comprehensive synaptic wiring diagrams of large neural circuits has created the field of connectomics and given rise to a number of open research questions. One such question is whether it is possible to reconstruct the information stored in ...

2023

Numerical simulation of immiscible incompressible viscous, viscoelastic and elastic multiphase flows

Léo Aurélio Diserens

A unified numerical framework is presented for the modelling of multiphasic viscoelastic and elastic flows. The rheologies considered range from incompressible Newtonian or Oldroyd-B viscoelastic fluids to Neo-Hookean elastic solids. The model is formulate ...

EPFL2023

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

Maximum Independent Set: Self-Training through Dynamic Programming

Volkan Cevher, Grigorios Chrysos, Efstratios Panteleimon Skoulakis

This work presents a graph neural network (GNN) framework for solving the maximum independent set (MIS) problem, inspired by dynamic programming (DP). Specifically, given a graph, we propose a DP-like recursive algorithm based on GNNs that firstly construc ...

2023

Results on Sparse Integer Programming and Geometric Independent Sets

Jana Tabea Cslovjecsek

An integer linear program is a problem of the form max{c^T x : Ax=b, x >= 0, x integer}, where A is in Z^(n x m), b in Z^m, and c in Z^n. Solving an integer linear program is NP-hard in general, but there are several assumptions for which it becomes fixed ...

EPFL2023

Effect of wettability and textile architecture on fluid displacement and pore formation during infiltration of carbon fibrous preforms

Véronique Michaud, Baris Çaglar, Helena Luisa Teixido Pedarros

We seek to address how air entrapment mechanisms during infiltration are influenced by the wetting characteristics of the fluid and the pore network formed by the reinforcement. To this end, we evaluated the behavior of two model fluids with different surf ...

Elsevier Sci Ltd2023

Approximation Algorithms for Allocation and Network Design

Etienne Michel François Bamas

In this thesis, we give new approximation algorithms for some NP-hard problems arising in resource allocation and network design. As a resource allocation problem, we study the Santa Claus problem (also known as the MaxMin Fair Allocation problem) in which ...

EPFL2023

Visualization and modelling of dynamic flow in fibrous preforms for liquid composite molding

Helena Luisa Teixido Pedarros

Nowadays, Liquid Composite Molding techniques are often used to manufacture high quality fiber reinforced composite parts at a relatively low cost. These involve an infiltration process, in which a liquid resin is forced to ingress into a dry fibrous porou ...

EPFL2023

Phenomenological theory of variational quantum ground-state preparation

Ivano Tavernelli, Giuseppe Carleo

The variational approach is a cornerstone of computational physics, considering both conventional and quantum computing computational platforms. The variational quantum eigensolver algorithm aims to prepare the ground state of a Hamiltonian exploiting para ...

Amer Physical Soc2023

Dielectric Elastomer Actuator-Based Valveless Impedance-Driven Pumping for Meso- and Macroscale Applications

Yves Perriard, Yoan René Cyrille Civet, Thomas Guillaume Martinez, Armando Matthieu Walter, Amine Benouhiba, Francesco Clavica, Silje Ekroll Jahren

Impedance pumps are simple designs that allow the generation or amplification of flow. They are fluid-filled systems based on flexible tubing connected to tubing with different impedances. A periodic off-center compression of the flexible tubing causes the ...

2023