Imagen de Portada

Statistically controllable microstructure reconstruction framework for heterogeneous materials using sliced-Wasserstein metric and neural networks

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Ma, Zhenchuan, Teng, Qizhi, Yan, Pengcheng, Li, Lindong, Gerke, Kirill M., Karsanina, Marina V., He, Xiaohai
Formato:	Preprint
Publicado:	2025
Materias:	Computational Physics Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Fast rigid alignment of heterogeneous images in sliced Wasserstein distance
por: Shi, Yunpeng, et al.
Publicado: (2025)

Physically recurrent neural network for rate and path-dependent heterogeneous materials in a finite strain framework
por: Maia, M. A., et al.
Publicado: (2024)

The role of surface material properties on the behavior of ionic liquids in nanoconfinement: A critical review and perspective
por: Nesterova, Irina, et al.
Publicado: (2024)

Microsecond-scale sucrose conformational dynamics in aqueous solution via molecular dynamics methods
por: Deshchenya, Vladimir, et al.
Publicado: (2025)

Wasserstein distributional adversarial training for deep neural networks
por: Bai, Xingjian, et al.
Publicado: (2025)

Consistent machine learning for topology optimization with microstructure-dependent neural network material models
por: Vijayakumaran, Harikrishnan, et al.
Publicado: (2024)

Wasserstein barycenters on metric graphs
por: Bertrand, Jérôme, et al.
Publicado: (2026)

$L^2$ over Wasserstein: Statistical Analysis for Optimal Transport
por: Passeggeri, Riccardo, et al.
Publicado: (2026)

An efficient Wasserstein-distance approach for reconstructing jump-diffusion processes using parameterized neural networks
por: Xia, Mingtao, et al.
Publicado: (2024)

Max-sliced 2-Wasserstein distance
por: Boedihardjo, March T.
Publicado: (2024)

On sliced Cramér metrics
por: Leeb, William
Publicado: (2025)

Efficient reconstruction of multidimensional random field models with heterogeneous data using stochastic neural networks
por: Xia, Mingtao, et al.
Publicado: (2025)

Mixture of neural fields for heterogeneous reconstruction in cryo-EM
por: Levy, Axel, et al.
Publicado: (2024)

Stochastic reconstruction of multiphase composite microstructures using statistics-encoded neural network for poro/micro-mechanical modelling
por: Fu, Jinlong, et al.
Publicado: (2025)

Structured generalized sliced Wasserstein distance for keV X-ray polarization analysis with Gas Pixel Detector
por: Ai, Pengcheng, et al.
Publicado: (2026)

Predicting void nucleation in microstructure with convolutional neural networks
por: Anantharanga, Abhijith Thoopul, et al.
Publicado: (2025)

A computational framework to predict weld integrity and microstructural heterogeneity: application to hydrogen transmission
por: Wijnen, J., et al.
Publicado: (2024)

Liouville PDE-based sliced-Wasserstein flow
por: Cooper, Jayshawn, et al.
Publicado: (2025)

Geometry and analytic properties of the sliced Wasserstein space
por: Park, Sangmin, et al.
Publicado: (2023)

Sharp bounds for max-sliced Wasserstein distances
por: Boedihardjo, March T.
Publicado: (2024)

Materials design based on a material-motif network and heterogeneous graphs
por: Aryal, Anoj, et al.
Publicado: (2026)

Sliced Wasserstein Geodesics and Equivalence Wasserstein and Sliced Wasserstein metrics
por: Hopper, John Seale
Publicado: (2024)

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks
por: Xia, Mingtao, et al.
Publicado: (2025)

Arbitrage equilibrium and the emergence of universal microstructure in deep neural networks
por: Venkatasubramanian, Venkat, et al.
Publicado: (2024)

Generalized invariants meet constitutive neural networks: A novel framework for hyperelastic materials
por: Martonová, Denisa, et al.
Publicado: (2025)

Repulsive Monte Carlo on the sphere for the sliced Wasserstein distance
por: Petrovic, Vladimir, et al.
Publicado: (2025)

Taming the Poisonous
por: Gerke, Barbara
Publicado: (2021)

Narrativas biográficas na formação docente do campo: memórias e experiências do curso Escola da Terra Capixaba
por: Janinha Gerke
Publicado: (2022)

Organization and Management of a Curriculum Library: A Description of Procedures and Practices.
por: Gerke, Ray
Publicado: (1981)

Decomposing heterogeneous dynamical systems with graph neural networks
por: Allier, Cédric, et al.
Publicado: (2024)

Inverse design of anisotropic microstructures using physics-augmented neural networks
por: Jadoon, Asghar A., et al.
Publicado: (2024)

Graph neural network for multitask prediction of rheological and microstructural behavior in suspensions
por: Aminimajd, Armin, et al.
Publicado: (2026)

Event generation and consistency tests with sliced Wasserstein distance in high-energy physics
por: Pan, Chu-Cheng, et al.
Publicado: (2023)

Artificial neural networks reconstruct missing perikymata in worn teeth
por: Mario Modesto‐Mata, et al.
Publicado: (2024)

Bounding adapted Wasserstein metrics
por: Blanchet, Jose, et al.
Publicado: (2024)

Conceptual framework for the application of deep neural networks to surface composition reconstruction from Mercury's exospheric data
por: Kazakov, Adrian, et al.
Publicado: (2025)

A sliced Wasserstein and diffusion approach to random coefficient models
por: Lim, Keunwoo, et al.
Publicado: (2025)

Sharp comparisons between sliced and standard $1$-Wasserstein distances
por: Carlier, Guillaume, et al.
Publicado: (2025)

An improved central limit theorem for the empirical sliced Wasserstein distance
por: Rodríguez-Vítores, David, et al.
Publicado: (2025)

Statistical tuning of artificial neural network
por: Mohamad, Mohamad Yamen AL, et al.
Publicado: (2024)