Publications related to Predicting chaotic statistics with unstable invariant tori

Related publications (16)

Fair Decentralized Learning

Anne-Marie Kermarrec, Rishi Sharma, Marinus Abraham de Vos, Sayan Biswas

Decentralized learning (DL) is an emerging approach that enables nodes to collaboratively train a machine learning model without sharing raw data. In many application domains, such as healthcare, this approach faces challenges due to the high level of hete ...

IEEE2025

Fast Parallel Algorithms for Enumeration of Simple, Temporal, and Hop-Constrained Cycles

Paolo Ienne, Kubilay Atasu, Jovan Blanusa

Finding cycles in directed graphs enables important applications in various domains such as finance, biology, chemistry, and network science. However, as the size of graph datasets continues to grow, it becomes increasingly difficult to discover cycles wit ...

arXiv2023

Tight Vector Bin Packing with Few Small Items via Fast Exact Matching in Multigraphs

Adam Teodor Polak, Alexandra Anna Lassota

We solve the Bin Packing problem in O^*(2^k) time, where k is the number of items less or equal to one third of the bin capacity. This parameter measures the distance from the polynomially solvable case of only large (i.e., greater than one third) items. O ...

Schloss Dagstuhl – Leibniz-Zentrum fur Informatik2022

Putting ridesharing to the test: efficient and scalable solutions and the power of dynamic vehicle relocation

Boi Faltings, Panayiotis Danassis, Marija Sakota

We study the optimization of large-scale, real-time ridesharing systems and propose a modular design methodology, Component Algorithms for Ridesharing (CAR). We evaluate a diverse set of CARs (14 in total), focusing on the key algorithmic components of rid ...

SPRINGER2022

Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks

Chen Liu

In the last decade, deep neural networks have achieved tremendous success in many fields of machine learning. However, they are shown vulnerable against adversarial attacks: well-designed, yet imperceptible, perturbations can make the state-of-the-art deep ...

EPFL2022

Variational methods for finding periodic orbits in the incompressible Navier Stokes equations

Tobias Schneider, Jeremy Peter Parker

Unstable periodic orbits are believed to underpin the dynamics of turbulence, but by their nature are hard to find computationally. We present a family of methods to converge such unstable periodic orbits for the incompressible Navier-Stokes equations, bas ...

Cambridge University Press2022

Ride-sharing with inflexible drivers in the Paris metropolitan area

Patrick Stefan Adriaan Stokkink

In ride-sharing, commuters with similar itineraries share a vehicle for their trip. Despite its clear benefits in terms of reduced congestion, ride-sharing is not yet widely accepted. We propose a specific ride-sharing variant, where drivers are completely ...

SPRINGER2022

Model order reduction of flow based on a modular geometrical approximation of blood vessels

Simone Deparis, Luca Pegolotti

We are interested in a reduced order method for the efficient simulation of blood flow in arteries. The blood dynamics is modeled by means of the incompressible Navier–Stokes equations. Our algorithm is based on an approximated domain-decomposition of the ...

2021

A Self-powered Ambient Light Power Measurement Platform with Time-domain Readout

Catherine Dehollain, Roberto La Rosa

Internet of Things (IoT) provides more and more adequate infrastructures for many applications and advanced services. This scenario promotes the ability to satisfy the growing demand for innovative measuring systems, increasingly popular consisting of inte ...

IEEE2021

Estimation of discrete choice models with hybrid stochastic adaptive batch size algorithms

Michel Bierlaire, Virginie Janine Camille Lurkin, Timothy Michael Hillel, Gael Lederrey

The emergence of Big Data has enabled new research perspectives in the discrete choice community. While the techniques to estimate Machine Learning models on a massive amount of data are well established, these have not yet been fully explored for the esti ...

2021

Simultaneous Perturbation Stochastic Approximation of the Quantum Fisher Information

Giuseppe Carleo, Julien Sebastian Gacon

The Quantum Fisher Information matrix (QFIM) is a central metric in promising algorithms, such as Quantum Natural Gradient Descent and Variational Quantum Imaginary Time Evolution. Computing the full QFIM for a model with d parameters, however, is computat ...

VEREIN FORDERUNG OPEN ACCESS PUBLIZIERENS QUANTENWISSENSCHAF2021

Detection of Bad PMU Data using Machine Learning Techniques

Sidi-Rachid Cherkaoui, Alexandra Cameron Karpilow

This project presents a pre-State Estimation method for the detection of Bad Data (BD) in Phasor Measurement Units (PMUs) using correlation analysis and a Neural Network Classifier. Presented in this paper is the algorithm design, the steps for generating ...

IEEE2020

A Homotopy Method with Adaptive Basis Selection for Computing Multiple Solutions of Differential Equations

Jan Sickmann Hesthaven

The homotopy continuation method has been widely used to compute multiple solutions of nonlinear differential equations, but the computational cost grows exponentially based on the traditional finite difference and finite element discretizations. In this w ...

SPRINGER/PLENUM PUBLISHERS2020

Development of a real-time algorithm for detection of the divertor detachment radiation front using multi-spectral imaging

Christian Gabriel Theiler, Kevin Henricus Annemarie Verhaegh, Wouter Vijvers, Artur Perek

In this paper we present a novel algorithm to extract the optical plasma boundary and radiation front for detached divertor plasmas. We show that reliable detection of the divertor leg and radiation front is possible using lightweight image processing tool ...

IOP Publishing Ltd2020

Design and Decoding of Irregular LDPC Codes Based on Discrete Message Passing

Andreas Peter Burg

We consider discrete message passing (MP) decoding of low-density parity check (LDPC) codes based on information-optimal symmetric look-up table (LUT). A link between discrete message labels and the associated log-likelihood ratio values (defined in terms ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Three-Dimensional Optical Diffraction Tomography With Lippmann-Schwinger Model

Michaël Unser, Demetri Psaltis, Emmanuel Emilien Louis Soubies, Thanh-An Michel Pham, Joowon Lim, Ahmed Bassam Sayed Ayoub Mohamed Emam

A broad class of imaging modalities involve the resolution of an inverse-scattering problem. Among them, three-dimensional optical diffraction tomography (ODT) comes with its own challenges. These include a limited range of views, a large size of the sampl ...

2020