Self-adaptive weighting and sampling for physics-informed neural networks
Fuente:
arXiv
Saved in:
| Main Authors: | Chen, Wenqian, Howard, Amanda, Stinis, Panos |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator networks
by: Chen, Wenqian, et al.
Published: (2024)
by: Chen, Wenqian, et al.
Published: (2024)
ArGEnT: Arbitrary Geometry-encoded Transformer for Operator Learning
by: Chen, Wenqian, et al.
Published: (2026)
by: Chen, Wenqian, et al.
Published: (2026)
Finite basis Kolmogorov-Arnold networks: domain decomposition for data-driven and physics-informed problems
by: Howard, Amanda A., et al.
Published: (2024)
by: Howard, Amanda A., et al.
Published: (2024)
Simulating Three-dimensional Turbulence with Physics-informed Neural Networks
by: Wang, Sifan, et al.
Published: (2025)
by: Wang, Sifan, et al.
Published: (2025)
Improving the accuracy of physics-informed neural networks via last-layer retraining
by: Qadeer, Saad, et al.
Published: (2026)
by: Qadeer, Saad, et al.
Published: (2026)
Multifidelity domain decomposition-based physics-informed neural networks and operators for time-dependent problems
by: Heinlein, Alexander, et al.
Published: (2024)
by: Heinlein, Alexander, et al.
Published: (2024)
Lagged backward-compatible physics-informed neural networks for unsaturated soil consolidation analysis
by: Li, Dong, et al.
Published: (2026)
by: Li, Dong, et al.
Published: (2026)
Astral: training physics-informed neural networks with error majorants
by: Fanaskov, Vladimir, et al.
Published: (2024)
by: Fanaskov, Vladimir, et al.
Published: (2024)
Hard-constraining Neumann boundary conditions in physics-informed neural networks via Fourier feature embeddings
by: Straub, Christopher, et al.
Published: (2025)
by: Straub, Christopher, et al.
Published: (2025)
PINNverse: Accurate parameter estimation in differential equations from noisy data with constrained physics-informed neural networks
by: Almanstötter, Marius, et al.
Published: (2025)
by: Almanstötter, Marius, et al.
Published: (2025)
Bridging quantum and classical computing for partial differential equations through multifidelity machine learning
by: Jacob, Bruno, et al.
Published: (2025)
by: Jacob, Bruno, 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)
What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications
by: Williams, Emily, et al.
Published: (2024)
by: Williams, Emily, et al.
Published: (2024)
Improving ideal MHD equilibrium accuracy with physics-informed neural networks
by: Thun, Timo, et al.
Published: (2025)
by: Thun, Timo, et al.
Published: (2025)
A quatum inspired neural network for geometric modeling
by: Du, Weitao, et al.
Published: (2024)
by: Du, Weitao, et al.
Published: (2024)
Physics-informed neural networks need a physicist to be accurate: the case of mass and heat transport in Fischer-Tropsch catalyst particles
by: Nikolaienko, Tymofii, et al.
Published: (2024)
by: Nikolaienko, Tymofii, et al.
Published: (2024)
A reduced-order derivative-informed neural operator for subsurface fluid-flow
by: Park, Jeongjin, et al.
Published: (2025)
by: Park, Jeongjin, 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)
SE3Set: Harnessing equivariant hypergraph neural networks for molecular representation learning
by: Wu, Hongfei, et al.
Published: (2024)
by: Wu, Hongfei, et al.
Published: (2024)
Towards physics-informed neural networks for landslide prediction
by: Dahal, Ashok, et al.
Published: (2024)
by: Dahal, Ashok, et al.
Published: (2024)
Enforcing hidden physics in physics-informed neural networks
by: Chen, Nanxi, et al.
Published: (2025)
by: Chen, Nanxi, et al.
Published: (2025)
Randomness and signal propagation in physics-informed neural networks (PINNs): A neural PDE perspective
by: Tucny, Jean-Michel, et al.
Published: (2025)
by: Tucny, Jean-Michel, et al.
Published: (2025)
Higher-order-ReLU-KANs (HRKANs) for solving physics-informed neural networks (PINNs) more accurately, robustly and faster
by: So, Chi Chiu, et al.
Published: (2024)
by: So, Chi Chiu, et al.
Published: (2024)
Efficient Transformer-Inspired Variants of Physics-Informed Deep Operator Networks
by: Wei, Zhi-Feng, et al.
Published: (2025)
by: Wei, Zhi-Feng, et al.
Published: (2025)
Data-driven building energy efficiency prediction using physics-informed neural networks
by: Michalakopoulos, Vasilis, et al.
Published: (2023)
by: Michalakopoulos, Vasilis, et al.
Published: (2023)
SPIKANs: Separable Physics-Informed Kolmogorov-Arnold Networks
by: Jacob, Bruno, et al.
Published: (2024)
by: Jacob, Bruno, et al.
Published: (2024)
Multifidelity Kolmogorov-Arnold Networks
by: Howard, Amanda A., et al.
Published: (2024)
by: Howard, Amanda A., et al.
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)
Inference of dynamical gene regulatory networks from single-cell data with physics informed neural networks
by: Mircea, Maria, et al.
Published: (2024)
by: Mircea, Maria, et al.
Published: (2024)
Binary structured physics-informed neural networks for solving equations with rapidly changing solutions
by: Liu, Yanzhi, et al.
Published: (2024)
by: Liu, Yanzhi, et al.
Published: (2024)
Efficient kernel surrogates for neural network-based regression
by: Qadeer, Saad, et al.
Published: (2023)
by: Qadeer, Saad, et al.
Published: (2023)
A comparative analysis of a neural network with calculated weights and a neural network with random generation of weights based on the training dataset size
by: Geidarov, Polad
Published: (2025)
by: Geidarov, Polad
Published: (2025)
Graph neural networks informed locally by thermodynamics
by: Tierz, Alicia, et al.
Published: (2024)
by: Tierz, Alicia, et al.
Published: (2024)
Two-stage initial-value iterative physics-informed neural networks for simulating solitary waves of nonlinear wave equations
by: Song, Jin, et al.
Published: (2024)
by: Song, Jin, et al.
Published: (2024)
Topological derivative approach for deep neural network architecture adaptation
by: Krishnanunni, C G, et al.
Published: (2025)
by: Krishnanunni, C G, et al.
Published: (2025)
Enhancing classification accuracy through chaos
by: Stinis, Panos
Published: (2026)
by: Stinis, Panos
Published: (2026)
Do deep neural networks utilize the weight space efficiently?
by: Koyun, Onur Can, et al.
Published: (2024)
by: Koyun, Onur Can, et al.
Published: (2024)
Grey-informed neural network for time-series forecasting
by: Xie, Wanli, et al.
Published: (2024)
by: Xie, Wanli, et al.
Published: (2024)
Permutation-equivariant quantum convolutional neural networks
by: Das, Sreetama, et al.
Published: (2024)
by: Das, Sreetama, et al.
Published: (2024)
Detecting hidden structures from a static loading experiment: topology optimization meets physics-informed neural networks
by: Mowlavi, Saviz, et al.
Published: (2023)
by: Mowlavi, Saviz, et al.
Published: (2023)
Similar Items
-
Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator networks
by: Chen, Wenqian, et al.
Published: (2024) -
ArGEnT: Arbitrary Geometry-encoded Transformer for Operator Learning
by: Chen, Wenqian, et al.
Published: (2026) -
Finite basis Kolmogorov-Arnold networks: domain decomposition for data-driven and physics-informed problems
by: Howard, Amanda A., et al.
Published: (2024) -
Simulating Three-dimensional Turbulence with Physics-informed Neural Networks
by: Wang, Sifan, et al.
Published: (2025) -
Improving the accuracy of physics-informed neural networks via last-layer retraining
by: Qadeer, Saad, et al.
Published: (2026)