GFN: A graph feedforward network for resolution-invariant reduced operator learning in multifidelity applications
Fuente:
arXiv
Saved in:
| Main Authors: | Morrison, Oisín M., Pichi, Federico, Hesthaven, Jan S. |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Neural empirical interpolation method for nonlinear model reduction
by: Hirsch, Max, et al.
Published: (2024)
by: Hirsch, Max, et al.
Published: (2024)
Convergence and Sketching-Based Efficient Computation of Neural Tangent Kernel Weights in Physics-Based Loss
by: Hirsch, Max, et al.
Published: (2025)
by: Hirsch, Max, et al.
Published: (2025)
A multifidelity approach to continual learning for physical systems
by: Howard, Amanda, et al.
Published: (2023)
by: Howard, Amanda, et al.
Published: (2023)
Latent Dynamics Graph Convolutional Networks for model order reduction of parameterized time-dependent PDEs
by: Tomada, Lorenzo, et al.
Published: (2026)
by: Tomada, Lorenzo, et al.
Published: (2026)
Machine learning enhanced real-time aerodynamic forces prediction based on sparse pressure sensor inputs
by: Duan, Junming, et al.
Published: (2023)
by: Duan, Junming, et al.
Published: (2023)
Time Extrapolation with Graph Convolutional Autoencoder and Tensor Train Decomposition
by: Chen, Yuanhong, et al.
Published: (2025)
by: Chen, Yuanhong, et al.
Published: (2025)
Integrating Artificial Intelligence, Physics, and Internet of Things: A Framework for Cultural Heritage Conservation
by: Valentino, Carmine, et al.
Published: (2026)
by: Valentino, Carmine, et al.
Published: (2026)
Nonlinear model reduction for transport-dominated problems
by: Hesthaven, Jan S., et al.
Published: (2026)
by: Hesthaven, Jan S., et al.
Published: (2026)
Resolution invariant deep operator network for PDEs with complex geometries
by: Huang, Jianguo, et al.
Published: (2024)
by: Huang, Jianguo, et al.
Published: (2024)
Group-invariant tensor train networks for supervised learning
by: Sprangers, Brent, et al.
Published: (2022)
by: Sprangers, Brent, et al.
Published: (2022)
Orthogonal greedy algorithm for linear operator learning with shallow neural network
by: Lin, Ye, et al.
Published: (2025)
by: Lin, Ye, et al.
Published: (2025)
A randomized algorithm to solve reduced rank operator regression
by: Turri, Giacomo, et al.
Published: (2023)
by: Turri, Giacomo, et al.
Published: (2023)
Conservative approximation-based feedforward neural network for WENO schemes
by: Park, Kwanghyuk, et al.
Published: (2025)
by: Park, Kwanghyuk, et al.
Published: (2025)
Deciphering and integrating invariants for neural operator learning with various physical mechanisms
by: Zhang, Rui, et al.
Published: (2023)
by: Zhang, Rui, et al.
Published: (2023)
On latent dynamics learning in nonlinear reduced order modeling
by: Farenga, Nicola, et al.
Published: (2024)
by: Farenga, Nicola, et al.
Published: (2024)
Nonlinear model reduction for operator learning
by: Eivazi, Hamidreza, et al.
Published: (2024)
by: Eivazi, Hamidreza, et al.
Published: (2024)
Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation
by: Ivagnes, Anna, et al.
Published: (2022)
by: Ivagnes, Anna, et al.
Published: (2022)
Variational operator learning: A unified paradigm marrying training neural operators and solving partial differential equations
by: Xu, Tengfei, et al.
Published: (2023)
by: Xu, Tengfei, et al.
Published: (2023)
Fast Numerical Approximation of Parabolic Problems Using Model Order Reduction and the Laplace Transform
by: Henríquez, Fernando, et al.
Published: (2024)
by: Henríquez, Fernando, et al.
Published: (2024)
Fast Numerical Approximation of Linear, Second-Order Hyperbolic Problems Using Model Order Reduction and the Laplace Transform
by: Henriquez, Fernando, et al.
Published: (2024)
by: Henriquez, Fernando, et al.
Published: (2024)
Learning cardiac activation and repolarization times with operator learning
by: Centofanti, Edoardo, et al.
Published: (2025)
by: Centofanti, Edoardo, et al.
Published: (2025)
Deep set based operator learning with uncertainty quantification
by: Ma, Lei, et al.
Published: (2025)
by: Ma, Lei, et al.
Published: (2025)
Variationally correct operator learning: Reduced basis neural operator with a posteriori error estimation
by: Qiu, Yuan, et al.
Published: (2025)
by: Qiu, Yuan, et al.
Published: (2025)
A novel data generation scheme for surrogate modelling with deep operator networks
by: Choubey, Shivam, et al.
Published: (2024)
by: Choubey, Shivam, et al.
Published: (2024)
Mesh motion in fluid-structure interaction with deep operator networks
by: Hellan, Ottar
Published: (2024)
by: Hellan, Ottar
Published: (2024)
Physics-informed neural networks for operator equations with stochastic data
by: Escapil-Inchauspé, Paul, et al.
Published: (2022)
by: Escapil-Inchauspé, Paul, et al.
Published: (2022)
Optimal deep learning of holomorphic operators between Banach spaces
by: Adcock, Ben, et al.
Published: (2024)
by: Adcock, Ben, et al.
Published: (2024)
Out-of-distributional risk bounds for neural operators with applications to the Helmholtz equation
by: Benitez, J. Antonio Lara, et al.
Published: (2023)
by: Benitez, J. Antonio Lara, et al.
Published: (2023)
Approximation by non-symmetric networks for cross-domain learning
by: Mhaskar, Hrushikesh
Published: (2023)
by: Mhaskar, Hrushikesh
Published: (2023)
Dimension reduction for derivative-informed operator learning: An analysis of approximation errors
by: Luo, Dingcheng, et al.
Published: (2025)
by: Luo, Dingcheng, et al.
Published: (2025)
Pseudo-Hamiltonian neural networks for learning partial differential equations
by: Eidnes, Sølve, et al.
Published: (2023)
by: Eidnes, Sølve, et al.
Published: (2023)
Certified machine learning: A posteriori error estimation for physics-informed neural networks
by: Hillebrecht, Birgit, et al.
Published: (2022)
by: Hillebrecht, Birgit, et al.
Published: (2022)
Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification
by: Blechschmidt, Jan, et al.
Published: (2025)
by: Blechschmidt, Jan, et al.
Published: (2025)
Learned iterative networks: An operator learning perspective
by: Hauptmann, Andreas, et al.
Published: (2025)
by: Hauptmann, Andreas, et al.
Published: (2025)
DPG loss functions for learning parameter-to-solution maps by neural networks
by: Castillo, Pablo Cortés, et al.
Published: (2025)
by: Castillo, Pablo Cortés, et al.
Published: (2025)
Probabilistic operator learning: generative modeling and uncertainty quantification for foundation models of differential equations
by: Zhang, Benjamin J., et al.
Published: (2025)
by: Zhang, Benjamin J., et al.
Published: (2025)
Augmented data and neural networks for robust epidemic forecasting: application to COVID-19 in Italy
by: Dimarco, Giacomo, et al.
Published: (2025)
by: Dimarco, Giacomo, et al.
Published: (2025)
PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs
by: Brivio, Simone, et al.
Published: (2024)
by: Brivio, Simone, et al.
Published: (2024)
Approximation and learning of anisotropic and mixed smooth functions by deep ReLU neural networks
by: Yang, Yunfei, et al.
Published: (2026)
by: Yang, Yunfei, et al.
Published: (2026)
Error analysis for finite element operator learning methods for solving parametric second-order elliptic PDEs
by: Hong, Youngjoon, et al.
Published: (2024)
by: Hong, Youngjoon, et al.
Published: (2024)
Similar Items
-
Neural empirical interpolation method for nonlinear model reduction
by: Hirsch, Max, et al.
Published: (2024) -
Convergence and Sketching-Based Efficient Computation of Neural Tangent Kernel Weights in Physics-Based Loss
by: Hirsch, Max, et al.
Published: (2025) -
A multifidelity approach to continual learning for physical systems
by: Howard, Amanda, et al.
Published: (2023) -
Latent Dynamics Graph Convolutional Networks for model order reduction of parameterized time-dependent PDEs
by: Tomada, Lorenzo, et al.
Published: (2026) -
Machine learning enhanced real-time aerodynamic forces prediction based on sparse pressure sensor inputs
by: Duan, Junming, et al.
Published: (2023)