DPG loss functions for learning parameter-to-solution maps by neural networks
Fuente:
arXiv
Saved in:
| Main Authors: | Castillo, Pablo Cortés, Dahmen, Wolfgang, Gopalakrishnan, Jay |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
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)
Analysis of FEAST spectral approximations using the DPG discretization
by: Gopalakrishnan, Jay, et al.
Published: (2019)
by: Gopalakrishnan, Jay, et al.
Published: (2019)
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)
Pseudo-Hamiltonian neural networks for learning partial differential equations
by: Eidnes, Sølve, et al.
Published: (2023)
by: Eidnes, Sølve, et al.
Published: (2023)
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)
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)
Geometrical structures of digital fluctuations in parameter space of neural networks trained with adaptive momentum optimization
by: Netay, Igor V.
Published: (2024)
by: Netay, Igor V.
Published: (2024)
A deformation-based framework for learning solution mappings of PDEs defined on varying domains
by: Xiao, Shanshan, et al.
Published: (2024)
by: Xiao, Shanshan, et al.
Published: (2024)
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)
On the optimal approximation of Sobolev and Besov functions using deep ReLU neural networks
by: Yang, Yunfei
Published: (2024)
by: Yang, Yunfei
Published: (2024)
Learning smooth functions in high dimensions: from sparse polynomials to deep neural networks
by: Adcock, Ben, et al.
Published: (2024)
by: Adcock, Ben, et al.
Published: (2024)
Fixed-budget online adaptive learning for physics-informed neural networks. Towards parameterized problem inference
by: Nguyen, Thi Nguyen Khoa, et al.
Published: (2022)
by: Nguyen, Thi Nguyen Khoa, et al.
Published: (2022)
Random test functions, $H^{-1}$ norm equivalence, and stochastic variational physics-informed neural networks
by: Marcondes, Diego
Published: (2026)
by: Marcondes, Diego
Published: (2026)
Long-term simulation of physical and mechanical behaviors using curriculum-transfer-learning based physics-informed neural networks
by: Guo, Yuan, et al.
Published: (2025)
by: Guo, Yuan, et al.
Published: (2025)
Generalizing the SINDy approach with nested neural networks
by: Fiorini, Camilla, et al.
Published: (2024)
by: Fiorini, Camilla, et al.
Published: (2024)
Training Hamiltonian neural networks without backpropagation
by: Rahma, Atamert, et al.
Published: (2024)
by: Rahma, Atamert, et al.
Published: (2024)
Gradient-free training of recurrent neural networks
by: Bolager, Erik Lien, et al.
Published: (2024)
by: Bolager, Erik Lien, et al.
Published: (2024)
Stable neural networks and connections to continuous dynamical systems
by: Ehrhardt, Matthias J., et al.
Published: (2025)
by: Ehrhardt, Matthias J., et al.
Published: (2025)
Energy stable neural network for gradient flow equations
by: Wu, Yue, et al.
Published: (2023)
by: Wu, Yue, et al.
Published: (2023)
Pseudo-differential-enhanced physics-informed neural networks
by: Gracyk, Andrew
Published: (2026)
by: Gracyk, Andrew
Published: (2026)
Exact and approximate error bounds for physics-informed neural networks
by: Chantada, Augusto T., et al.
Published: (2024)
by: Chantada, Augusto T., et al.
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)
Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov Arnold Networks
by: Wang, Yizheng, et al.
Published: (2024)
by: Wang, Yizheng, et al.
Published: (2024)
Approximation rates of quantum neural networks for periodic functions via Jackson's inequality
by: Neufeld, Ariel, et al.
Published: (2025)
by: Neufeld, Ariel, et al.
Published: (2025)
Natural Riemannian gradient for learning functional tensor networks
by: Klug, Nikolas, et al.
Published: (2026)
by: Klug, Nikolas, et al.
Published: (2026)
Solving engineering eigenvalue problems with neural networks using the Rayleigh quotient
by: Rowan, Conor, et al.
Published: (2025)
by: Rowan, Conor, et al.
Published: (2025)
A note on the adjoint method for neural ordinary differential equation network
by: Hu, Pipi
Published: (2024)
by: Hu, Pipi
Published: (2024)
Two-hidden-layer ReLU neural networks and finite elements
by: Jin, Pengzhan
Published: (2024)
by: Jin, Pengzhan
Published: (2024)
Improving physics-informed neural network extrapolation via transfer learning and adaptive activation functions
by: Papastathopoulos-Katsaros, Athanasios, et al.
Published: (2025)
by: Papastathopoulos-Katsaros, Athanasios, et al.
Published: (2025)
Learning solution operator of dynamical systems with diffusion maps kernel ridge regression
by: Song, Jiwoo, et al.
Published: (2025)
by: Song, Jiwoo, et al.
Published: (2025)
Consistent machine learning for topology optimization with microstructure-dependent neural network material models
by: Vijayakumaran, Harikrishnan, et al.
Published: (2024)
by: Vijayakumaran, Harikrishnan, et al.
Published: (2024)
Domain decomposition architectures and Gauss-Newton training for physics-informed neural networks
by: Heinlein, Alexander, et al.
Published: (2025)
by: Heinlein, Alexander, et al.
Published: (2025)
Macroscopic auxiliary asymptotic preserving neural networks for the linear radiative transfer equations
by: Li, Hongyan, et al.
Published: (2024)
by: Li, Hongyan, et al.
Published: (2024)
Memorization capacity of deep ReLU neural networks characterized by width and depth
by: Yang, Xin, et al.
Published: (2026)
by: Yang, Xin, et al.
Published: (2026)
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)
Structure-preserving neural networks for the regularized entropy-based closure of the Boltzmann moment system
by: Schotthöfer, Steffen, et al.
Published: (2024)
by: Schotthöfer, Steffen, et al.
Published: (2024)
A shallow physics-informed neural network for solving partial differential equations on surfaces
by: Hu, Wei-Fan, et al.
Published: (2022)
by: Hu, Wei-Fan, et al.
Published: (2022)
An extended physics informed neural network for preliminary analysis of parametric optimal control problems
by: Demo, Nicola, et al.
Published: (2021)
by: Demo, Nicola, et al.
Published: (2021)
Neural network-driven domain decomposition for efficient solutions to the Helmholtz equation
by: Dolean, Victorita, et al.
Published: (2025)
by: Dolean, Victorita, et al.
Published: (2025)
Approximation by non-symmetric networks for cross-domain learning
by: Mhaskar, Hrushikesh
Published: (2023)
by: Mhaskar, Hrushikesh
Published: (2023)
Similar Items
-
Variationally correct operator learning: Reduced basis neural operator with a posteriori error estimation
by: Qiu, Yuan, et al.
Published: (2025) -
Analysis of FEAST spectral approximations using the DPG discretization
by: Gopalakrishnan, Jay, et al.
Published: (2019) -
Approximation and learning of anisotropic and mixed smooth functions by deep ReLU neural networks
by: Yang, Yunfei, et al.
Published: (2026) -
Pseudo-Hamiltonian neural networks for learning partial differential equations
by: Eidnes, Sølve, et al.
Published: (2023) -
Orthogonal greedy algorithm for linear operator learning with shallow neural network
by: Lin, Ye, et al.
Published: (2025)