Search Results for author:
Found 8 papers, 3 papers with code
Coupled Laplacian Eigenmaps for Locally-Aware 3D Rigid Point Cloud Matching
In this work, we propose a new technique, based on graph Laplacian eigenmaps, to match point clouds by taking into account fine local structures.
A Simple and Robust Framework for Cross-Modality Medical Image Segmentation applied to Vision Transformers
In this work, we propose a simple framework to achieve fair image segmentation of multiple modalities using a single conditional model that adapts its normalization layers based on the input type, trained with non-registered interleaved mixed data.
A priori compression of convolutional neural networks for wave simulators
The time needed to make predictions or time required for training using the original Convolutional Neural Networks model would be cut significantly if there were fewer parameters to deal with.
Uncertainty quantification in a mechanical submodel driven by a Wasserstein-GAN
An architecture that support a random component is necessary for the construction of the stochastic model of the physical system for non-parametric uncertainties, since the goal is to learn the underlying probabilistic distribution of uncertainty in the data.
Uncertainty quantification for industrial design using dictionaries of reduced order models
The main contribution of this work is the application of the complete workflow to a real-life industrial model of an elastoviscoplastic high-pressure turbine blade subjected to thermal, centrifugal and pressure loadings, for the quantification of the uncertainty on dual quantities (such as the accumulated plastic strain and the stress tensor), generated by the uncertainty on the temperature loading field.
A modular U-Net for automated segmentation of X-ray tomography images in composite materials
X-ray Computed Tomography (XCT) techniques have evolved to a point that high-resolution data can be acquired so fast that classic segmentation methods are prohibitively cumbersome, demanding automated data pipelines capable of dealing with non-trivial 3D images.
Ranked #1 on
2D Semantic Segmentation
on GF-PA66 3D XCT
Data augmentation and feature selection for automatic model recommendation in computational physics
Classification algorithms have recently found applications in computational physics for the selection of numerical methods or models adapted to the environment and the state of the physical system.
Reduced Bond Graph via machine learning for nonlinear multiphysics dynamic systems
The output of the machine learning is a hybrid modeling that contains a reduced Bond Graph coupled to a simple artificial neural network.
