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A method and system for enforcing smoothness constraints on surface meshes from a graph convolutional neural network

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Publications associées (35)

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Neural-network quantum states for periodic systems in continuous space

Giuseppe Carleo, Gabriel Maria Pescia

We introduce a family of neural quantum states for the simulation of strongly interacting systems in the presence of spatial periodicity. Our variational state is parametrized in terms of a permutationally invariant part described by the Deep Sets neural-n ...

AMER PHYSICAL SOC2022

Reduced order modelling of nonaffine problems on parameterized NURBS multipatch geometries

Annalisa Buffa, Pablo Antolin Sanchez, Margarita Chasapi

This contribution explores the combined capabilities of reduced basis methods and IsoGeometric Analysis (IGA) in the context of parameterized partial differential equations. The introduction of IGA enables a unified simulation framework based on a single g ...

2022

Meshing of Spiny Neuronal Morphologies using Union Operators

Felix Schürmann, Marwan Muhammad Ahmed Abdellah, Nadir Roman Guerrero, Alessandro Enrico Foni, Elvis Boci, Juan José García Cantero

Neurons are characterized by thin and long interleaving arborizations in which creating accurate mesh models of their cellular membranes is challenging. While union operators are central for CAD/CAM modeling and computer graphics applications, their applic ...

2022

A virtual microstructure generator for 3D stone masonry walls

Katrin Beyer, Mahmoud S. M. Shaqfa

Detailed micromodel simulations of stone masonry walls require as input a 3D mesh that represents a realistic arrangement of stones in the masonry wall. In this paper, we constructed the first 3D masonry microstructures to derive 2D and 3D finite or discre ...

ELSEVIER2022

A Non-Exponential Transmittance Model for Volumetric Scene Representations

Wenzel Alban Jakob, Delio Aleardo Vicini

We introduce a novel transmittance model to improve the volumetric representation of 3D scenes. The model can represent opaque surfaces in the volumetric light transport framework. Volumetric representations are useful for complex scenes, and become increa ...

ASSOC COMPUTING MACHINERY2021

Adaptive analysis-aware defeaturing

Ondine Gabrielle Chanon

Removing geometrical details from a complex domain is a classical operation in computer aided design for simulation and manufacturing. This procedure simplifies the meshing process, and it enables faster simulations with less memory requirements. However, ...

EPFL2022

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

Sketch2Mesh: Reconstructing and Editing 3D Shapes from Sketches

Pascal Fua, Benoît Alain René Guillard, Edoardo Remelli, Pierre Mathieu Yvernay

Reconstructing 3D shape from 2D sketches has long been an open problem because the sketches only provide very sparse and ambiguous information. In this paper, we use an encoder/decoder architecture for the sketch to mesh translation. When integrated into a ...

IEEE2021

An arbitrary-order Cell Method with block-diagonal mass-matrices for the time-dependent 2D Maxwell equations

Bernard Kapidani

We introduce a new numerical method for the time-dependent Maxwell equations on unstructured meshes in two space dimensions. This relies on the introduction of a new mesh, which is the barycentric-dual cellular complex of the starting simplicial mesh, and ...

2021

Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs

Nicolas Henri Bernard Flammarion, Loucas Pillaud-Vivien, Etienne Patrice Boursier

The training of neural networks by gradient descent methods is a cornerstone of the deep learning revolution. Yet, despite some recent progress, a complete theory explaining its success is still missing. This article presents, for orthogonal input vectors, ...

NeurIPS2022

Multi-Level Monte Carlo Methods for Uncertainty Quantification and Risk-Averse Optimisation

Sundar Subramaniam Ganesh

This work aims to study the effects of wind uncertainties in civil engineering structural design. Optimising the design of a structure for safety or operability without factoring in these uncertainties can result in a design that is not robust to these per ...

EPFL2022

Loss landscape and symmetries in Neural Networks

Mario Geiger

Neural networks (NNs) have been very successful in a variety of tasks ranging from machine translation to image classification. Despite their success, the reasons for their performance are still not well-understood. This thesis explores two main themes: lo ...

EPFL2021

How to teach neural networks to mesh: Application on 2-D simplicial contours

François Avellan, Pascal Clausen, Alexis Papagiannopoulos

A machine learning meshing scheme for the generation of 2-D simplicial meshes is proposed based on the predictions of neural networks. The data extracted from meshed contours are utilized to train neural networks which are used to approximate the number of ...

2021

Adaptive Self-Sealing Suction-Based Soft Robotic Gripper

Metin Sitti, Sukho Song

While suction cups prevail as common gripping tools for a wide range of real-world parts and surfaces, they often fail to seal the contact interface when engaging with irregular shapes and textured surfaces. In this work, the authors propose a suction-base ...

2021

Tessellation and lmprovement of Simplicial Meshes using Neural Networks

Alexis Papagiannopoulos

Modern mesh generation addresses the development of robust algorithms that construct a discrete representation of the geometry into polytopal elements conforming to divergent properties: (i) fidelity to complex geometrical features, (ii) support for high s ...

EPFL2021