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Related publications (30)

Constructing periodic orbits of high-dimensional chaotic systems by an adjoint-based variational method

Tobias Schneider, Sajjad Azimi, Omid Ashtari

Chaotic dynamics in systems ranging from low-dimensional nonlinear differential equations to high-dimensional spatiotemporal systems including fluid turbulence is supported by nonchaotic, exactly recurring time-periodic solutions of the governing equations ...

2022

Thermal Modelling and Experimental Validation in the Perspective of Tool Steel Laser Polishing

Eric Boillat, Alexandre Masserey, Bastian Meylan

Laser polishing (LP) is a process that allows a significant reduction of the surface roughness of a metal workpiece via re-melting a shallow layer of material. However, the practical use of LP is limited due to the difficulty of satisfying the high surface ...

MDPI2022

A Shadow Perspective on Equivariant Hochschild Homologies

Kathryn Hess Bellwald, Inbar Klang

Shadows for bicategories, defined by Ponto, provide a useful framework that generalizes classical and topological Hochschild homology. In this paper, we define Hochschild-type invariants for monoids in a symmetric monoidal, simplicial model category V, as ...

OXFORD UNIV PRESS2022

Visual Focus of Attention Estimation in 3D Scene with an Arbitrary Number of Targets

Jean-Marc Odobez, Rémy Alain Siegfried

Visual Focus of Attention (VFOA) estimation in conversation is challenging as it relies on difficult to estimate information (gaze) combined with scene features like target positions and other contextual information (speaking status) allowing to disambigua ...

IEEE COMPUTER SOC2021

Global bifurcation at isolated singular points of the Hadamard derivative

Charles Stuart

Consider F is an element of C(RxX,Y) such that F(lambda, 0) = 0 for all lambda is an element of R, where X and Y are Banach spaces. Bifurcation from the line Rx{0} of trivial solutions is investigated in cases where F(lambda, center dot ) need not be Frech ...

2021

Masksembles for Uncertainty Estimation

Pascal Fua, Pierre Bruno Baqué, Nikita Durasov, Timur Bagautdinov

Deep neural networks have amply demonstrated their prowess but estimating the reliability of their predictions remains challenging. Deep Ensembles are widely considered as being one of the best methods for generating uncertainty estimates but are very expe ...

2021

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

Last iterate convergence of SGD for Least-Squares in the Interpolation regime

Nicolas Henri Bernard Flammarion, Aditya Vardhan Varre, Loucas Pillaud-Vivien

Motivated by the recent successes of neural networks that have the ability to fit the data perfectly \emph{and} generalize well, we study the noiseless model in the fundamental least-squares setup. We assume that an optimum predictor fits perfectly inputs ...

Advances in Neural Information Processing Systems 20212021

Conjugation spaces are cohomologically pure

Jérôme Scherer

Conjugation spaces are equipped with an involution such that the fixed points have the same mod 2 cohomology (as a graded vector space, a ring, and even an unstable algebra) but with all degrees divided by 2, generalizing the classical examples of complex ...

WILEY2021

Prediction of the reaction forces of spiral-groove gas journal bearings by artificial neural network regression models

Jürg Alexander Schiffmann, Elia Iseli

This paper presents neural network regression models for predicting the nonlinear static and linearized dynamic reaction forces of spiral grooved gas journal bearings. The partial differential equations (PDEs) are sampled, based on a full factorial and ran ...

2021

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