Physically Recurrent Neural Networks for Computational Homogenization of Composite Materials with Microscale Debonding
Fuente:
arXiv
Saved in:
| Main Authors: | Kovács, N., Maia, M. A., Rocha, I. B. C. M., Furtado, C., Camanho, P. P., van der Meer, F. P. |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Uncertainty Quantification in Multiscale Modeling of Polymer Composite Materials Using Physically Recurrent Neural Networks
by: Kovács, N., et al.
Published: (2025)
by: Kovács, N., et al.
Published: (2025)
A Microstructure-based Graph Neural Network for Accelerating Multiscale Simulations
by: Storm, J., et al.
Published: (2024)
by: Storm, J., et al.
Published: (2024)
Physically recurrent neural network for rate and path-dependent heterogeneous materials in a finite strain framework
by: Maia, M. A., et al.
Published: (2024)
by: Maia, M. A., et al.
Published: (2024)
Surrogate-based multiscale analysis of experiments on thermoplastic composites under off-axis loading
by: Maia, M. A., et al.
Published: (2025)
by: Maia, M. A., et al.
Published: (2025)
Mixing Data-Driven and Physics-Based Constitutive Models using Uncertainty-Driven Phase Fields
by: Storm, J., et al.
Published: (2025)
by: Storm, J., et al.
Published: (2025)
Effects of Interpolation Error and Bias on the Random Mesh Finite Element Method for Inverse Problems
by: Poot, Anne, et al.
Published: (2025)
by: Poot, Anne, et al.
Published: (2025)
A Bayesian Approach to Modeling Finite Element Discretization Error
by: Poot, Anne, et al.
Published: (2023)
by: Poot, Anne, et al.
Published: (2023)
Neural network methods for Neumann series problems of Perron-Frobenius operators
by: Udomworarat, T., et al.
Published: (2025)
by: Udomworarat, T., et al.
Published: (2025)
Computational identification of the source domain in an inverse problem of potential theory
by: Vabishchevich, P. N.
Published: (2025)
by: Vabishchevich, P. N.
Published: (2025)
RUNNs: Ritz-Uzawa Neural Networks for Solving Variational Problems
by: Herrera, Pablo, et al.
Published: (2026)
by: Herrera, Pablo, et al.
Published: (2026)
Decapodes: A Diagrammatic Tool for Representing, Composing, and Computing Spatialized Partial Differential Equations
by: Morris, Luke, et al.
Published: (2024)
by: Morris, Luke, et al.
Published: (2024)
Inexact Uzawa-Double Deep Ritz Method for Weak Adversarial Neural Networks
by: Benny-Chacko, Emin, et al.
Published: (2025)
by: Benny-Chacko, Emin, et al.
Published: (2025)
Computational Math with Neural Networks is Hard
by: Feischl, Michael, et al.
Published: (2025)
by: Feischl, Michael, et al.
Published: (2025)
Evolving finite elements for advection diffusion with an evolving interface
by: Elliott, C. M., et al.
Published: (2022)
by: Elliott, C. M., et al.
Published: (2022)
Discontinuity Computing using Physics-Informed Neural Network
by: Liu, Li, et al.
Published: (2022)
by: Liu, Li, et al.
Published: (2022)
On Separation of Variables
by: Viazminsky, C. P.
Published: (2002)
by: Viazminsky, C. P.
Published: (2002)
WENO scheme on characteristics for the equilibrium dispersive model of chromatography with generalized Langmuir isotherms
by: Donat, R., et al.
Published: (2024)
by: Donat, R., et al.
Published: (2024)
Variational Principles for the Helmholtz equation: application to Finite Element and Neural Network approximations
by: Makrakis, G., et al.
Published: (2025)
by: Makrakis, G., et al.
Published: (2025)
Observations on Recurrent Loss in the Neural Network Model of a Partial Differential Equation: the Advection-Diffusion Equation
by: Reeger, Jonah A.
Published: (2025)
by: Reeger, Jonah A.
Published: (2025)
Optimizing Variational Physics-Informed Neural Networks Using Least Squares
by: Uriarte, Carlos, et al.
Published: (2024)
by: Uriarte, Carlos, et al.
Published: (2024)
Inf-sup theory for the quasi-static Biot's equations in poroelasticity
by: Kreuzer, C., et al.
Published: (2024)
by: Kreuzer, C., et al.
Published: (2024)
Inf-sup stable discretization of the quasi-static Biot's equations in poroelasticity
by: Kreuzer, C., et al.
Published: (2024)
by: Kreuzer, C., et al.
Published: (2024)
Mixing Data‐Driven and Physics‐Based Constitutive Models Using Uncertainty‐Driven Phase Fields
by: Joep Storm, et al.
Published: (2025)
by: Joep Storm, et al.
Published: (2025)
On the Stability and Convergence of Physics Informed Neural Networks
by: Gazoulis, Dimitrios, et al.
Published: (2023)
by: Gazoulis, Dimitrios, et al.
Published: (2023)
Solving advection equations with reduction multigrids on GPUs
by: Dargaville, S., et al.
Published: (2025)
by: Dargaville, S., et al.
Published: (2025)
Coarsening and parallelism with reduction multigrids for hyperbolic Boltzmann transport
by: Dargaville, S., et al.
Published: (2024)
by: Dargaville, S., et al.
Published: (2024)
Quantum Recurrent Neural Networks with Encoder-Decoder for Time-Dependent Partial Differential Equations
by: Chen, Yuan, et al.
Published: (2025)
by: Chen, Yuan, et al.
Published: (2025)
Coupling Physics Informed Neural Networks with External Solvers
by: Halder, Rahul, et al.
Published: (2025)
by: Halder, Rahul, et al.
Published: (2025)
Non-self-adjoint sixth-order eigenvalue problems arising from clamped elastic thin films on closed domains
by: Papanicolaou, N C, et al.
Published: (2025)
by: Papanicolaou, N C, et al.
Published: (2025)
Efficient WENO schemes for nonuniform grids
by: Martí, M. C., et al.
Published: (2024)
by: Martí, M. C., et al.
Published: (2024)
Physics Informed Neural Networks for heat conduction with phase change
by: Madir, Bahae-Eddine, et al.
Published: (2024)
by: Madir, Bahae-Eddine, et al.
Published: (2024)
Computational Insights into Orthotropic Fracture: Crack-Tip Fields in Strain-Limiting Materials under Non-Uniform Loads
by: Ghosh, Saugata, et al.
Published: (2025)
by: Ghosh, Saugata, et al.
Published: (2025)
Lippmann–Schwinger Spectrum, Composite Materials Eigenstates and Their Role in Computational Homogenization
by: C. Bellis, et al.
Published: (2025)
by: C. Bellis, et al.
Published: (2025)
Error estimates for full discretization of Cahn--Hilliard equation with dynamic boundary conditions
by: Bullerjahn, Nils, et al.
Published: (2024)
by: Bullerjahn, Nils, et al.
Published: (2024)
A posteriori error estimates for parabolic partial differential equations on stationary surfaces
by: Kovács, Balázs, et al.
Published: (2024)
by: Kovács, Balázs, et al.
Published: (2024)
Stochastic Quadrature Rules for Solving PDEs using Neural Networks
by: Taylor, Jamie M., et al.
Published: (2025)
by: Taylor, Jamie M., et al.
Published: (2025)
Mathematical Modeling of Boson-Fermion Stars in the Generalized Scalar-Tensor Theories of Gravity
by: Boyadjiev, T. L., et al.
Published: (1999)
by: Boyadjiev, T. L., et al.
Published: (1999)
New Numerical Algorithm for Modeling of Boson-Fermion Stars in Dilatonic Gravity
by: Boyadjiev, T. L., et al.
Published: (2000)
by: Boyadjiev, T. L., et al.
Published: (2000)
A Physics-Informed Neural Network approach for compartmental epidemiological models
by: Millevoi, Caterina, et al.
Published: (2023)
by: Millevoi, Caterina, et al.
Published: (2023)
Segmentation-Based Regression for Quantum Neural Networks
by: Hateley, James C.
Published: (2025)
by: Hateley, James C.
Published: (2025)
Similar Items
-
Uncertainty Quantification in Multiscale Modeling of Polymer Composite Materials Using Physically Recurrent Neural Networks
by: Kovács, N., et al.
Published: (2025) -
A Microstructure-based Graph Neural Network for Accelerating Multiscale Simulations
by: Storm, J., et al.
Published: (2024) -
Physically recurrent neural network for rate and path-dependent heterogeneous materials in a finite strain framework
by: Maia, M. A., et al.
Published: (2024) -
Surrogate-based multiscale analysis of experiments on thermoplastic composites under off-axis loading
by: Maia, M. A., et al.
Published: (2025) -
Mixing Data-Driven and Physics-Based Constitutive Models using Uncertainty-Driven Phase Fields
by: Storm, J., et al.
Published: (2025)