Image de couverture de livre

Physics-informed neural networks with curriculum training for poroelastic flow and deformation processes

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteur principal:	Bekele, Yared W.
Format:	Preprint
Publié:	2024
Sujets:	Computational Engineering, Finance, and Science
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

GeoSim.AI: AI assistants for numerical simulations in geomechanics
par: Bekele, Yared W.
Publié: (2025)

GeoMCP: A Trustworthy Framework for AI-Assisted Analytical Geotechnical Engineering
par: Bekele, Yared W.
Publié: (2026)

Deep Learning for One-dimensional Consolidation
par: Bekele, Yared W.
Publié: (2020)

Physics-informed neural networks for form-finding of unilateral membrane structures
par: Sibille, Luigi, et autres
Publié: (2026)

Automated machine learning for physics-informed convolutional neural networks
par: Zhou, Wanyun, et autres
Publié: (2024)

Physics-informed neural networks for parameter learning of wildfire spreading
par: Vogiatzoglou, Konstantinos, et autres
Publié: (2024)

PINN-MG: A physics-informed neural network for mesh generation
par: Wang, Min, et autres
Publié: (2025)

Energy-based physics-informed neural network for frictionless contact problems under large deformation
par: Bai, Jinshuai, et autres
Publié: (2024)

Surface profile recovery from electromagnetic field with physics--informed neural networks
par: Chen, Yuxuan, et autres
Publié: (2024)

Viscoelasticty with physics-augmented neural networks: Model formulation and training methods without prescribed internal variables
par: Rosenkranz, Max, et autres
Publié: (2024)

VW-PINNs: A volume weighting method for PDE residuals in physics-informed neural networks
par: Song, Jiahao, et autres
Publié: (2024)

Fast training of accurate physics-informed neural networks without gradient descent
par: Datar, Chinmay, et autres
Publié: (2024)

Gaussian process regression + deep neural network autoencoder for probabilistic surrogate modeling in nonlinear mechanics of solids
par: Deshpande, Saurabh, et autres
Publié: (2024)

Transfer learning-based physics-informed convolutional neural network for simulating flow in porous media with time-varying controls
par: Chen, Jungang, et autres
Publié: (2023)

Physics-augmented neural networks for constitutive modeling of hyperelastic geometrically exact beams
par: Schommartz, Jasper O., et autres
Publié: (2024)

Fitting micro-kinetic models to transient kinetics of temporal analysis of product reactors using kinetics-informed neural networks
par: Nai, Dingqi, et autres
Publié: (2024)

Simulation of parametrized cardiac electrophysiology in three dimensions using physics-informed neural networks
par: Gomez, Roshan Antony, et autres
Publié: (2025)

Learning solutions of parametric Navier-Stokes with physics-informed neural networks
par: Naderibeni, M., et autres
Publié: (2024)

Local learning for stable backpropagation-free neural network training towards physical learning
par: Guo, Yaqi, et autres
Publié: (2026)

I-FENN for thermoelasticity based on physics-informed temporal convolutional network (PI-TCN)
par: Abueidda, Diab W., et autres
Publié: (2023)

Domain decomposition of large neural network surrogate models
par: Gödde, Timm, et autres
Publié: (2026)

Learning coupled Allen-Cahn and Cahn-Hilliard phase-field equations using Physics-informed neural operator(PINO)
par: Gangmei, Gaijinliu, et autres
Publié: (2025)

A shifted interface approach for internal discontinuities in poroelastic media
par: Riley, David Michael, et autres
Publié: (2026)

Convolutional neural network based reduced order modeling for multiscale problems
par: Zhang, Xuhan, et autres
Publié: (2024)

Inverse design of anisotropic microstructures using physics-augmented neural networks
par: Jadoon, Asghar A., et autres
Publié: (2024)

Real-time design of architectural structures with differentiable mechanics and neural networks
par: Pastrana, Rafael, et autres
Publié: (2024)

Cross-attention-based bipartite graph neural network for coupled nodal and elemental field prediction in large-deformation sheet material forming
par: Zhao, Yingxue, et autres
Publié: (2026)

Nonlinear electro-elastic finite element analysis with neural network constitutive models
par: Klein, Dominik K., et autres
Publié: (2024)

Crack detection by holomorphic neural networks and transfer-learning-enhanced genetic optimization
par: Hund, Jonas, et autres
Publié: (2025)

A physics-augmented neural network framework for finite strain incompressible viscoelasticity
par: Kalina, Karl A., et autres
Publié: (2025)

PyTOaCNN: Topology optimization using an adaptive convolutional neural network in Python
par: Chadha, Khaish Singh, et autres
Publié: (2024)

Multiscale graph neural networks with adaptive mesh refinement for accelerating mesh-based simulations
par: Perera, Roberto, et autres
Publié: (2024)

Hydrogen reaction rate modeling based on convolutional neural network for large eddy simulation
par: Malé, Quentin, et autres
Publié: (2024)

Test-time data augmentation: improving predictions of recurrent neural network models of composites
par: Uvdal, Petter, et autres
Publié: (2024)

Sequential infinite-dimensional Bayesian optimal experimental design with derivative-informed latent attention neural operator
par: Go, Jinwoo, et autres
Publié: (2024)

GrainGNN: A dynamic graph neural network for predicting 3D grain microstructure
par: Qin, Yigong, et autres
Publié: (2024)

A physics-enhanced multi-modal fused neural network for predicting contamination length interval in pipeline
par: Du, Jian, et autres
Publié: (2024)

A novel boundary integrated neural networks for in plane fracture mechanics analysis of elastic and piezoelectric materials
par: Zhang, Peijun, et autres
Publié: (2025)

A novel Taguchi-based approach for optimizing neural network architectures: application to elastic short fiber composites
par: Nikzad, Mohammad Hossein, et autres
Publié: (2024)

Multiscale topology optimization of functionally graded lattice structures based on physics-augmented neural network material models
par: Stollberg, Jonathan, et autres
Publié: (2024)