Guardado en:
| Autor principal: | Marcondes, Diego |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2605.03542 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Physics-informed neural networks for operator equations with stochastic data
por: Escapil-Inchauspé, Paul, et al.
Publicado: (2022)
por: Escapil-Inchauspé, Paul, et al.
Publicado: (2022)
Pseudo-differential-enhanced physics-informed neural networks
por: Gracyk, Andrew
Publicado: (2026)
por: Gracyk, Andrew
Publicado: (2026)
Exact and approximate error bounds for physics-informed neural networks
por: Chantada, Augusto T., et al.
Publicado: (2024)
por: Chantada, Augusto T., et al.
Publicado: (2024)
Domain decomposition architectures and Gauss-Newton training for physics-informed neural networks
por: Heinlein, Alexander, et al.
Publicado: (2025)
por: Heinlein, Alexander, et al.
Publicado: (2025)
Optimal time sampling in physics-informed neural networks
por: Turinici, Gabriel
Publicado: (2024)
por: Turinici, Gabriel
Publicado: (2024)
Certified machine learning: A posteriori error estimation for physics-informed neural networks
por: Hillebrecht, Birgit, et al.
Publicado: (2022)
por: Hillebrecht, Birgit, et al.
Publicado: (2022)
A shallow physics-informed neural network for solving partial differential equations on surfaces
por: Hu, Wei-Fan, et al.
Publicado: (2022)
por: Hu, Wei-Fan, et al.
Publicado: (2022)
An extended physics informed neural network for preliminary analysis of parametric optimal control problems
por: Demo, Nicola, et al.
Publicado: (2021)
por: Demo, Nicola, et al.
Publicado: (2021)
Long-term simulation of physical and mechanical behaviors using curriculum-transfer-learning based physics-informed neural networks
por: Guo, Yuan, et al.
Publicado: (2025)
por: Guo, Yuan, et al.
Publicado: (2025)
Cell-induced densification and tether formation in fibrous extracellular matrices with biomimetic physics-informed neural networks
por: Lin, Anci, et al.
Publicado: (2026)
por: Lin, Anci, et al.
Publicado: (2026)
A decomposition-based robust training of physics-informed neural networks for nearly incompressible linear elasticity
por: Dick, Josef, et al.
Publicado: (2025)
por: Dick, Josef, et al.
Publicado: (2025)
Fixed-budget online adaptive learning for physics-informed neural networks. Towards parameterized problem inference
por: Nguyen, Thi Nguyen Khoa, et al.
Publicado: (2022)
por: Nguyen, Thi Nguyen Khoa, et al.
Publicado: (2022)
Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning
por: De Ryck, Tim, et al.
Publicado: (2024)
por: De Ryck, Tim, et al.
Publicado: (2024)
A physics-informed neural network framework for modeling obstacle-related equations
por: Bahja, Hamid El, et al.
Publicado: (2023)
por: Bahja, Hamid El, et al.
Publicado: (2023)
Symmetry group based domain decomposition to enhance physics-informed neural networks for solving partial differential equations
por: Liu, Ye, et al.
Publicado: (2024)
por: Liu, Ye, et al.
Publicado: (2024)
Improving physics-informed neural network extrapolation via transfer learning and adaptive activation functions
por: Papastathopoulos-Katsaros, Athanasios, et al.
Publicado: (2025)
por: Papastathopoulos-Katsaros, Athanasios, et al.
Publicado: (2025)
Unified theoretical guarantees for stability, consistency, and convergence in neural PDE solvers from non-IID data to physics-informed networks
por: Katende, Ronald
Publicado: (2024)
por: Katende, Ronald
Publicado: (2024)
Astral: training physics-informed neural networks with error majorants
por: Fanaskov, Vladimir, et al.
Publicado: (2024)
por: Fanaskov, Vladimir, et al.
Publicado: (2024)
Improving the accuracy of physics-informed neural networks via last-layer retraining
por: Qadeer, Saad, et al.
Publicado: (2026)
por: Qadeer, Saad, et al.
Publicado: (2026)
DPG loss functions for learning parameter-to-solution maps by neural networks
por: Castillo, Pablo Cortés, et al.
Publicado: (2025)
por: Castillo, Pablo Cortés, et al.
Publicado: (2025)
Solving stochastic partial differential equations using neural networks in the Wiener chaos expansion
por: Neufeld, Ariel, et al.
Publicado: (2024)
por: Neufeld, Ariel, et al.
Publicado: (2024)
VS-PINN: A fast and efficient training of physics-informed neural networks using variable-scaling methods for solving PDEs with stiff behavior
por: Ko, Seungchan, et al.
Publicado: (2024)
por: Ko, Seungchan, et al.
Publicado: (2024)
Physics-informed neural networks (PINNs) for numerical model error approximation and superresolution
por: Zhuang, Bozhou, et al.
Publicado: (2024)
por: Zhuang, Bozhou, et al.
Publicado: (2024)
Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov Arnold Networks
por: Wang, Yizheng, et al.
Publicado: (2024)
por: Wang, Yizheng, et al.
Publicado: (2024)
Fast training of accurate physics-informed neural networks without gradient descent
por: Datar, Chinmay, et al.
Publicado: (2024)
por: Datar, Chinmay, et al.
Publicado: (2024)
Convergence of physics-informed neural networks modeling time-harmonic wave fields
por: Schoder, Stefan, et al.
Publicado: (2025)
por: Schoder, Stefan, et al.
Publicado: (2025)
Approximation and learning of anisotropic and mixed smooth functions by deep ReLU neural networks
por: Yang, Yunfei, et al.
Publicado: (2026)
por: Yang, Yunfei, et al.
Publicado: (2026)
On the optimal approximation of Sobolev and Besov functions using deep ReLU neural networks
por: Yang, Yunfei
Publicado: (2024)
por: Yang, Yunfei
Publicado: (2024)
Learning smooth functions in high dimensions: from sparse polynomials to deep neural networks
por: Adcock, Ben, et al.
Publicado: (2024)
por: Adcock, Ben, et al.
Publicado: (2024)
HomPINNs: homotopy physics-informed neural networks for solving the inverse problems of nonlinear differential equations with multiple solutions
por: Zheng, Haoyang, et al.
Publicado: (2023)
por: Zheng, Haoyang, et al.
Publicado: (2023)
A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder
por: Kim, Youngkyu, et al.
Publicado: (2020)
por: Kim, Youngkyu, et al.
Publicado: (2020)
A physics informed neural network approach to simulating ice dynamics governed by the shallow ice approximation
por: Chawla, Kapil, et al.
Publicado: (2025)
por: Chawla, Kapil, et al.
Publicado: (2025)
Generalizing the SINDy approach with nested neural networks
por: Fiorini, Camilla, et al.
Publicado: (2024)
por: Fiorini, Camilla, et al.
Publicado: (2024)
Training Hamiltonian neural networks without backpropagation
por: Rahma, Atamert, et al.
Publicado: (2024)
por: Rahma, Atamert, et al.
Publicado: (2024)
Gradient-free training of recurrent neural networks
por: Bolager, Erik Lien, et al.
Publicado: (2024)
por: Bolager, Erik Lien, et al.
Publicado: (2024)
Physics-informed reduced order model with conditional neural fields
por: Kim, Minji, et al.
Publicado: (2024)
por: Kim, Minji, et al.
Publicado: (2024)
Stable neural networks and connections to continuous dynamical systems
por: Ehrhardt, Matthias J., et al.
Publicado: (2025)
por: Ehrhardt, Matthias J., et al.
Publicado: (2025)
Energy stable neural network for gradient flow equations
por: Wu, Yue, et al.
Publicado: (2023)
por: Wu, Yue, et al.
Publicado: (2023)
Approximation rates of quantum neural networks for periodic functions via Jackson's inequality
por: Neufeld, Ariel, et al.
Publicado: (2025)
por: Neufeld, Ariel, et al.
Publicado: (2025)
Splitting physics-informed neural networks for inferring the dynamics of integer- and fractional-order neuron models
por: Shekarpaz, Simin, et al.
Publicado: (2023)
por: Shekarpaz, Simin, et al.
Publicado: (2023)
Ejemplares similares
-
Physics-informed neural networks for operator equations with stochastic data
por: Escapil-Inchauspé, Paul, et al.
Publicado: (2022) -
Pseudo-differential-enhanced physics-informed neural networks
por: Gracyk, Andrew
Publicado: (2026) -
Exact and approximate error bounds for physics-informed neural networks
por: Chantada, Augusto T., et al.
Publicado: (2024) -
Domain decomposition architectures and Gauss-Newton training for physics-informed neural networks
por: Heinlein, Alexander, et al.
Publicado: (2025) -
Optimal time sampling in physics-informed neural networks
por: Turinici, Gabriel
Publicado: (2024)