Optimal time sampling in physics-informed neural networks
Fuente:
arXiv
Saved in:
| Main Author: | Turinici, Gabriel |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Astral: training physics-informed neural networks with error majorants
by: Fanaskov, Vladimir, et al.
Published: (2024)
by: Fanaskov, Vladimir, et al.
Published: (2024)
Softmax gradient policy for variance minimization and risk-averse multi armed bandits
by: Turinici, Gabriel
Published: (2026)
by: Turinici, Gabriel
Published: (2026)
Onflow: a model free, online portfolio allocation algorithm robust to transaction fees
by: Turinici, Gabriel, et al.
Published: (2023)
by: Turinici, Gabriel, et al.
Published: (2023)
Splitting physics-informed neural networks for inferring the dynamics of integer- and fractional-order neuron models
by: Shekarpaz, Simin, et al.
Published: (2023)
by: Shekarpaz, Simin, et al.
Published: (2023)
Huber-energy measure quantization
by: Turinici, Gabriel
Published: (2022)
by: Turinici, Gabriel
Published: (2022)
Pseudo-differential-enhanced physics-informed neural networks
by: Gracyk, Andrew
Published: (2026)
by: Gracyk, Andrew
Published: (2026)
Convergence of physics-informed neural networks modeling time-harmonic wave fields
by: Schoder, Stefan, et al.
Published: (2025)
by: Schoder, Stefan, et al.
Published: (2025)
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)
Model free collision aggregation for the computation of escape distributions
by: Laguzet, Laetitia, et al.
Published: (2024)
by: Laguzet, Laetitia, et al.
Published: (2024)
Tensorization of neural networks for improved privacy and interpretability
by: Monturiol, José Ramón Pareja, et al.
Published: (2025)
by: Monturiol, José Ramón Pareja, 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)
Physics-informed neural networks (PINNs) for numerical model error approximation and superresolution
by: Zhuang, Bozhou, et al.
Published: (2024)
by: Zhuang, Bozhou, et al.
Published: (2024)
Improving physics-informed DeepONets with hard constraints
by: Brecht, Rüdiger, et al.
Published: (2023)
by: Brecht, Rüdiger, et al.
Published: (2023)
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)
A physics informed neural network approach to simulating ice dynamics governed by the shallow ice approximation
by: Chawla, Kapil, et al.
Published: (2025)
by: Chawla, Kapil, et al.
Published: (2025)
Physics-informed neural networks and neural operators for a study of EUV electromagnetic wave diffraction from a lithography mask
by: Es'kin, Vasiliy A., et al.
Published: (2025)
by: Es'kin, Vasiliy A., et al.
Published: (2025)
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)
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)
Fast training of accurate physics-informed neural networks without gradient descent
by: Datar, Chinmay, et al.
Published: (2024)
by: Datar, Chinmay, et al.
Published: (2024)
Automatically Differentiable Model Updating (ADiMU): conventional, hybrid, and neural network material model discovery including history-dependency
by: Ferreira, Bernardo P., et al.
Published: (2025)
by: Ferreira, Bernardo P., et al.
Published: (2025)
A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder
by: Kim, Youngkyu, et al.
Published: (2020)
by: Kim, Youngkyu, et al.
Published: (2020)
Convergence of a L2 regularized Policy Gradient Algorithm for the Multi Armed Bandit
by: Anita, Stefana, et al.
Published: (2024)
by: Anita, Stefana, et al.
Published: (2024)
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)
Multi evolutional deep neural networks (Multi-EDNN)
by: Kim, Hadden, et al.
Published: (2024)
by: Kim, Hadden, et al.
Published: (2024)
A decomposition-based robust training of physics-informed neural networks for nearly incompressible linear elasticity
by: Dick, Josef, et al.
Published: (2025)
by: Dick, Josef, et al.
Published: (2025)
Random test functions, $H^{-1}$ norm equivalence, and stochastic variational physics-informed neural networks
by: Marcondes, Diego
Published: (2026)
by: Marcondes, Diego
Published: (2026)
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)
Cell-induced densification and tether formation in fibrous extracellular matrices with biomimetic physics-informed neural networks
by: Lin, Anci, et al.
Published: (2026)
by: Lin, Anci, et al.
Published: (2026)
Symmetry group based domain decomposition to enhance physics-informed neural networks for solving partial differential equations
by: Liu, Ye, et al.
Published: (2024)
by: Liu, Ye, 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)
Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning
by: De Ryck, Tim, et al.
Published: (2024)
by: De Ryck, Tim, et al.
Published: (2024)
A physics-informed neural network framework for modeling obstacle-related equations
by: Bahja, Hamid El, et al.
Published: (2023)
by: Bahja, Hamid El, et al.
Published: (2023)
Unified theoretical guarantees for stability, consistency, and convergence in neural PDE solvers from non-IID data to physics-informed networks
by: Katende, Ronald
Published: (2024)
by: Katende, Ronald
Published: (2024)
Parameterized Physics-informed Neural Networks for Parameterized PDEs
by: Cho, Woojin, et al.
Published: (2024)
by: Cho, Woojin, et al.
Published: (2024)
VS-PINN: A fast and efficient training of physics-informed neural networks using variable-scaling methods for solving PDEs with stiff behavior
by: Ko, Seungchan, et al.
Published: (2024)
by: Ko, Seungchan, et al.
Published: (2024)
Derivative-informed neural operator acceleration of geometric MCMC for infinite-dimensional Bayesian inverse problems
by: Cao, Lianghao, et al.
Published: (2024)
by: Cao, Lianghao, et al.
Published: (2024)
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)
High-fidelity Multiphysics Modelling for Rapid Predictions Using Physics-informed Parallel Neural Operator
by: Yuan, Biao, et al.
Published: (2025)
by: Yuan, Biao, et al.
Published: (2025)
Do physics-informed neural networks (PINNs) need to be deep? Shallow PINNs using the Levenberg-Marquardt algorithm
by: Shahab, Muhammad Luthfi, et al.
Published: (2026)
by: Shahab, Muhammad Luthfi, et al.
Published: (2026)
Similar Items
-
Astral: training physics-informed neural networks with error majorants
by: Fanaskov, Vladimir, et al.
Published: (2024) -
Softmax gradient policy for variance minimization and risk-averse multi armed bandits
by: Turinici, Gabriel
Published: (2026) -
Onflow: a model free, online portfolio allocation algorithm robust to transaction fees
by: Turinici, Gabriel, et al.
Published: (2023) -
Splitting physics-informed neural networks for inferring the dynamics of integer- and fractional-order neuron models
by: Shekarpaz, Simin, et al.
Published: (2023) -
Huber-energy measure quantization
by: Turinici, Gabriel
Published: (2022)