Saved in:
| Main Authors: | Almanstötter, Marius, Vetter, Roman, Iber, Dagmar |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.05248 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
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)
Reply to: Assessing the precision of morphogen gradients in neural tube development
by: Vetter, Roman, et al.
Published: (2024)
by: Vetter, Roman, et al.
Published: (2024)
Self-adaptive weighting and sampling for physics-informed neural networks
by: Chen, Wenqian, et al.
Published: (2025)
by: Chen, Wenqian, et al.
Published: (2025)
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)
Physics-informed neural networks for the shallow-water equations on the sphere
by: Bihlo, Alex, et al.
Published: (2021)
by: Bihlo, Alex, et al.
Published: (2021)
Distributed physics informed neural network for data-efficient solution to partial differential equations
by: Dwivedi, Vikas, et al.
Published: (2019)
by: Dwivedi, Vikas, et al.
Published: (2019)
On-site estimation of battery electrochemical parameters via transfer learning based physics-informed neural network approach
by: Yeregui, Josu, et al.
Published: (2025)
by: Yeregui, Josu, et al.
Published: (2025)
Improving ideal MHD equilibrium accuracy with physics-informed neural networks
by: Thun, Timo, et al.
Published: (2025)
by: Thun, Timo, et al.
Published: (2025)
Breakeven complexity: A new perspective on neural partial differential equation solvers
by: Zhang, Yijing, et al.
Published: (2026)
by: Zhang, Yijing, et al.
Published: (2026)
Block removal for large language models through constrained binary optimization
by: Jansen, David, et al.
Published: (2026)
by: Jansen, David, et al.
Published: (2026)
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)
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)
PolyHoop: Soft particle and tissue dynamics with topological transitions
by: Vetter, Roman, et al.
Published: (2023)
by: Vetter, Roman, et al.
Published: (2023)
Simulating Organogenesis in COMSOL Multiphysics: Tissue Patterning with Directed Cell Migration
by: Mederacke, Malte, et al.
Published: (2025)
by: Mederacke, Malte, et al.
Published: (2025)
HomPINNs: homotopy physics-informed neural networks for solving the inverse problems of nonlinear differential equations with multiple solutions
by: Zheng, Haoyang, et al.
Published: (2023)
by: Zheng, Haoyang, et al.
Published: (2023)
A quatum inspired neural network for geometric modeling
by: Du, Weitao, et al.
Published: (2024)
by: Du, Weitao, 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)
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)
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)
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)
Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks
by: Hamreras, Safa, et al.
Published: (2025)
by: Hamreras, Safa, 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)
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)
Neural delay differential equations: learning non-Markovian closures for partially known dynamical systems
by: Monsel, Thibault, et al.
Published: (2024)
by: Monsel, Thibault, et al.
Published: (2024)
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)
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)
Sharp-PINNs: staggered hard-constrained physics-informed neural networks for phase field modelling of corrosion
by: Chen, Nanxi, et al.
Published: (2025)
by: Chen, Nanxi, et al.
Published: (2025)
Learning advisor networks for noisy image classification
by: Ricci, Simone, et al.
Published: (2022)
by: Ricci, Simone, et al.
Published: (2022)
Computing the gradients with respect to all parameters of a quantum neural network using a single circuit
by: He, Guang Ping
Published: (2023)
by: He, Guang Ping
Published: (2023)
Invertible Koopman neural operator for data-driven modeling of partial differential equations
by: Jin, Yuhong, et al.
Published: (2025)
by: Jin, Yuhong, et al.
Published: (2025)
Permutation-equivariant quantum convolutional neural networks
by: Das, Sreetama, et al.
Published: (2024)
by: Das, Sreetama, et al.
Published: (2024)
Functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks
by: Agata, Ryoichiro, et al.
Published: (2026)
by: Agata, Ryoichiro, et al.
Published: (2026)
Graph neural networks informed locally by thermodynamics
by: Tierz, Alicia, et al.
Published: (2024)
by: Tierz, Alicia, et al.
Published: (2024)
Graph neural network for colliding particles with an application to sea ice floe modeling
by: Zhu, Ruibiao
Published: (2026)
by: Zhu, Ruibiao
Published: (2026)
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)
Enforcing hidden physics in physics-informed neural networks
by: Chen, Nanxi, et al.
Published: (2025)
by: Chen, Nanxi, et al.
Published: (2025)
Fragment size density estimator for shrinkage-induced fracture based on a physics-informed neural network
by: Ito, Shin-ichi
Published: (2025)
by: Ito, Shin-ichi
Published: (2025)
Grey-informed neural network for time-series forecasting
by: Xie, Wanli, et al.
Published: (2024)
by: Xie, Wanli, et al.
Published: (2024)
Similar Items
-
Hard-constraining Neumann boundary conditions in physics-informed neural networks via Fourier feature embeddings
by: Straub, Christopher, et al.
Published: (2025) -
Reply to: Assessing the precision of morphogen gradients in neural tube development
by: Vetter, Roman, et al.
Published: (2024) -
Self-adaptive weighting and sampling for physics-informed neural networks
by: Chen, Wenqian, et al.
Published: (2025) -
Lagged backward-compatible physics-informed neural networks for unsaturated soil consolidation analysis
by: Li, Dong, et al.
Published: (2026) -
Astral: training physics-informed neural networks with error majorants
by: Fanaskov, Vladimir, et al.
Published: (2024)