Engineering application of physics-informed neural networks for Saint-Venant torsion
Fuente:
arXiv
Saved in:
| Main Authors: | Jo, Su Yeong, Park, Sanghyeon, Ko, Seungchan, Park, Jongcheon, Kim, Hosung, Lee, Sangseung, Jeon, Joongoo |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
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)
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)
Node Assigned physics-informed neural networks for thermal-hydraulic system simulation: CVH/FL module
by: Shin, Jeesuk, et al.
Published: (2025)
by: Shin, Jeesuk, et al.
Published: (2025)
Comparison of CNN-based deep learning architectures for unsteady CFD acceleration on small datasets
by: Khanal, Sangam, et al.
Published: (2025)
by: Khanal, Sangam, et al.
Published: (2025)
XRePIT: A deep learning-computational fluid dynamics hybrid framework implemented in OpenFOAM for fast, robust, and scalable unsteady simulations
by: Baral, Shilaj, et al.
Published: (2025)
by: Baral, Shilaj, et al.
Published: (2025)
Error analysis for finite element operator learning methods for solving parametric second-order elliptic PDEs
by: Hong, Youngjoon, et al.
Published: (2024)
by: Hong, Youngjoon, et al.
Published: (2024)
CURing Large Models: Compression via CUR Decomposition
by: Park, Sanghyeon, et al.
Published: (2025)
by: Park, Sanghyeon, et al.
Published: (2025)
A Numerical Method for Coupling Parameterized Physics-Informed Neural Networks and FDM for Advanced Thermal-Hydraulic System Simulation
by: Shin, Jeesuk, et al.
Published: (2026)
by: Shin, Jeesuk, et al.
Published: (2026)
Sobolev Approximation of Deep ReLU Networks in Log-Barron Space
by: Song, Changhoon, et al.
Published: (2026)
by: Song, Changhoon, et al.
Published: (2026)
FRAIN to Train: A Fast-and-Reliable Solution for Decentralized Federated Learning
by: Park, Sanghyeon, et al.
Published: (2025)
by: Park, Sanghyeon, et al.
Published: (2025)
Visualizing the loss landscapes of physics-informed neural networks
by: Rowan, Conor, et al.
Published: (2026)
by: Rowan, Conor, et al.
Published: (2026)
Enforcing hidden physics in physics-informed neural networks
by: Chen, Nanxi, et al.
Published: (2025)
by: Chen, Nanxi, et al.
Published: (2025)
LLM-Guided Communication for Cooperative Multi-Agent Reinforcement Learning
by: Bae, Sangjun, et al.
Published: (2026)
by: Bae, Sangjun, et al.
Published: (2026)
Exclusively Penalized Q-learning for Offline Reinforcement Learning
by: Yeom, Junghyuk, et al.
Published: (2024)
by: Yeom, Junghyuk, et al.
Published: (2024)
Advanced deep-reinforcement-learning methods for flow control: group-invariant and positional-encoding networks improve learning speed and quality
by: Jeon, Joongoo, et al.
Published: (2024)
by: Jeon, Joongoo, et al.
Published: (2024)
HyQuRP: Hybrid quantum-classical neural network with rotational and permutational equivariance
by: Park, Semin, et al.
Published: (2026)
by: Park, Semin, et al.
Published: (2026)
Strategies for training point distributions in physics-informed neural networks
by: Humagain, Santosh, et al.
Published: (2025)
by: Humagain, Santosh, et al.
Published: (2025)
$Δ$-PINNs: physics-informed neural networks on complex geometries
by: Costabal, Francisco Sahli, et al.
Published: (2022)
by: Costabal, Francisco Sahli, et al.
Published: (2022)
WGFINNs: Weak formulation-based GENERIC formalism informed neural networks
by: Park, Jun Sur Richard, et al.
Published: (2026)
by: Park, Jun Sur Richard, et al.
Published: (2026)
Hybrid deep additive neural networks
by: Kim, Gyu Min, et al.
Published: (2024)
by: Kim, Gyu Min, et al.
Published: (2024)
Pseudo-differential-enhanced physics-informed neural networks
by: Gracyk, Andrew
Published: (2026)
by: Gracyk, Andrew
Published: (2026)
Leveraging neural network interatomic potentials for a foundation model of chemistry
by: Kim, So Yeon, et al.
Published: (2025)
by: Kim, So Yeon, et al.
Published: (2025)
DiffInject: Revisiting Debias via Synthetic Data Generation using Diffusion-based Style Injection
by: Ko, Donggeun, et al.
Published: (2024)
by: Ko, Donggeun, et al.
Published: (2024)
An efficient wavelet-based physics-informed neural network for multiscale problems
by: Pandey, Himanshu, et al.
Published: (2024)
by: Pandey, Himanshu, et al.
Published: (2024)
Training deep physical neural networks with local physical information bottleneck
by: Wang, Hao, et al.
Published: (2026)
by: Wang, Hao, et al.
Published: (2026)
When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series
by: Park, Min-Yeong, et al.
Published: (2025)
by: Park, Min-Yeong, et al.
Published: (2025)
Towards physics-informed neural networks for landslide prediction
by: Dahal, Ashok, et al.
Published: (2024)
by: Dahal, Ashok, et al.
Published: (2024)
Efficient physics-informed neural networks using hash encoding
by: Huang, Xinquan, et al.
Published: (2023)
by: Huang, Xinquan, et al.
Published: (2023)
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)
GaborPINN: Efficient physics informed neural networks using multiplicative filtered networks
by: Huang, Xinquan, et al.
Published: (2023)
by: Huang, Xinquan, et al.
Published: (2023)
Residual resampling-based physics-informed neural network for neutron diffusion equations
by: Zhang, Heng, et al.
Published: (2024)
by: Zhang, Heng, et al.
Published: (2024)
Unified generalization analysis for physics informed neural networks
by: Hashimoto, Yuka, et al.
Published: (2026)
by: Hashimoto, Yuka, et al.
Published: (2026)
Optimal time sampling in physics-informed neural networks
by: Turinici, Gabriel
Published: (2024)
by: Turinici, Gabriel
Published: (2024)
Representations learnt by SGD and Adaptive learning rules: Conditions that vary sparsity and selectivity in neural networks
by: Park, Jin Hyun
Published: (2022)
by: Park, Jin Hyun
Published: (2022)
ST-LINK: Spatially-Aware Large Language Models for Spatio-Temporal Forecasting
by: Jeon, Hyotaek, et al.
Published: (2025)
by: Jeon, Hyotaek, et al.
Published: (2025)
A scaled TW-PINN: A physics-informed neural network for traveling wave solutions of reaction-diffusion equations with general coefficients
by: Han, Seungwan, et al.
Published: (2026)
by: Han, Seungwan, et al.
Published: (2026)
Equation identification for fluid flows via physics-informed neural networks
by: New, Alexander, et al.
Published: (2024)
by: New, Alexander, et al.
Published: (2024)
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)
ARMA Block: A CNN-Based Autoregressive and Moving Average Module for Long-Term Time Series Forecasting
by: Kim, Myung Jin, et al.
Published: (2025)
by: Kim, Myung Jin, et al.
Published: (2025)
SCOPE-FE: Structured Control of Operator and Pairwise Exploration for Feature Engineering
by: Park, Minhee, et al.
Published: (2026)
by: Park, Minhee, et al.
Published: (2026)
Similar Items
-
A decomposition-based robust training of physics-informed neural networks for nearly incompressible linear elasticity
by: Dick, Josef, et al.
Published: (2025) -
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) -
Node Assigned physics-informed neural networks for thermal-hydraulic system simulation: CVH/FL module
by: Shin, Jeesuk, et al.
Published: (2025) -
Comparison of CNN-based deep learning architectures for unsteady CFD acceleration on small datasets
by: Khanal, Sangam, et al.
Published: (2025) -
XRePIT: A deep learning-computational fluid dynamics hybrid framework implemented in OpenFOAM for fast, robust, and scalable unsteady simulations
by: Baral, Shilaj, et al.
Published: (2025)