Publications associées à Surface (géométrie analytique)

Automated image-based generation of finite element models for masonry buildings

Katrin Beyer, Radhakrishna Achanta, Bryan German Pantoja Rosero

To predict the response of masonry buildings to various types of loads, engineers use finite element models, specifically solid-element and macro-element models. For predicting masonry responses to seismic events in particular, equivalent frame models-a su ...

SPRINGER2023

Strategic Insights into Engineering Parameters Affecting Cell Type-Specific Uptake of DNA-Based Nanomaterials

Maartje Martina Cornelia Bastings, Alice Comberlato, Hugo José Rodriguez Franco, Marianna Mainardi Koga

DNA-based nanomaterials are gaining popularity as uniform and programmable bioengineering tools as a result of recent solutions to their weak stability under biological conditions. The DNA nanotechnology platform uniquely allows decoupling of engineering p ...

American Chemical Society2022

Robust numerical integration on curved polyhedra based on folded decompositions

Annalisa Buffa, Pablo Antolin Sanchez, Xiaodong Wei

We present a novel method to perform numerical integration over curved polyhedra enclosed by high-order parametric surfaces. Such a polyhedron is first decomposed into a set of triangular and/or rectangular pyramids, whose certain faces correspond to the g ...

ELSEVIER SCIENCE SA2022

Umbrella Meshes: Elastic Mechanisms for Freeform Shape Deployment

Mark Pauly, Florin Isvoranu, Uday Kusupati, Yingying Ren, Tian Chen, Davide Pellis

We present a computational inverse design framework for a new class of volumetric deployable structures that have compact rest states and deploy into bending-active 3D target surfaces. Umbrella meshes consist of elastic beams, rigid plates, and hinge joint ...

2022

Learning to Represent and Reconstruct 3D Deformable Objects

Jan Bednarík

Representing and reconstructing 3D deformable shapes are two tightly linked problems that have long been studied within the computer vision field. Deformable shapes are truly ubiquitous in the real world, whether be it specific object classes such as human ...

EPFL2022

A new characterization approach to study the mechanical behavior of silicon nanowires

Yusuf Leblebici, Zuhal Tasdemir, Mohammad Nasr Esfahani, Mustafa Yilmaz

This work proposes a new approach to characterize the mechanical properties of nanowires based on a combination of nanomechanical measurements and models. Silicon nanowires with a critical dimension of 90 nm and a length of 8 mu m obtained through a monoli ...

SPRINGER HEIDELBERG2021

Leveraging Spatial and Photometric Context for Calibrated Non-Lambertian Photometric Stereo

Pascal Fua, Engin Türetken, David Honzátko

The problem of estimating a surface shape from its observed reflectance properties still remains a challenging task in computer vision. The presence of global illumination effects such as inter-reflections or cast shadows makes the task particularly diffic ...

IEEE COMPUTER SOC2021

Serre-Tate theory for Calabi-Yau varieties

Maciej Emilian Zdanowicz

Classical Serre-Tate theory describes deformations of ordinary abelian varieties. It implies that every such variety has a canonical lift to characteristic zero and equips the base of its universal deformation with a Frobenius lifting and canonical multipl ...

WALTER DE GRUYTER GMBH2021

A parameter-free mechanistic model of the adhesive wear process of rough surfaces in sliding contact

Jean-François Molinari, Tobias Brink, Lucas Henri Galilée Frérot

In order to develop predictive wear laws, relevant material parameters and their influence on the wear rate need to be identified. Despite decades of research, there is no agreement on the mathematical form of wear equations and even the simplest models, s ...

2021

Shape Reconstruction by Learning Differentiable Surface Representations

Pascal Fua, Mathieu Salzmann, Shaifali Parashar, Jan Bednarík, Erhan Gündogdu

Generative models that produce point clouds have emerged as a powerful tool to represent 3D surfaces, and the best current ones rely on learning an ensemble of parametric representations. Unfortunately, they offer no control over the deformations of the su ...

IEEE2020