Image de couverture de livre

Mask-PINNs: Mitigating Internal Covariate Shift in Physics-Informed Neural Networks

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Jiang, Feilong, Hou, Xiaonan, Ye, Jianqiao, Xia, Min
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning Artificial Intelligence
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Densely Multiplied Physics Informed Neural Networks
par: Jiang, Feilong, et autres
Publié: (2024)

Element-wise Multiplication Based Deeper Physics-Informed Neural Networks
par: Jiang, Feilong, et autres
Publié: (2024)

Spatio-temporal Attention-based Hidden Physics-informed Neural Network for Remaining Useful Life Prediction
par: Jiang, Feilong, et autres
Publié: (2024)

Feature Mapping in Physics-Informed Neural Networks (PINNs)
par: Zeng, Chengxi, et autres
Publié: (2024)

Unveiling the optimization process of Physics Informed Neural Networks: How accurate and competitive can PINNs be?
par: Urbán, Jorge F., et autres
Publié: (2024)

Complex Physics-Informed Neural Network
par: Si, Chenhao, et autres
Publié: (2025)

Set-Valued Sensitivity Analysis of Deep Neural Networks
par: Wang, Xin, et autres
Publié: (2024)

GINN-KAN: Interpretability pipelining with applications in Physics Informed Neural Networks
par: Ranasinghe, Nisal, et autres
Publié: (2024)

Physics-Informed Neural Networks with Architectural Physics Embedding for Large-Scale Wave Field Reconstruction
par: Zhang, Huiwen, et autres
Publié: (2026)

CP-PINNs: Data-Driven Changepoints Detection in PDEs Using Online Optimized Physics-Informed Neural Networks
par: Dong, Zhikang, et autres
Publié: (2022)

From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning
par: Toscano, Juan Diego, et autres
Publié: (2024)

When and Why Adversarial Training Improves PINNs: A Neural Tangent Kernel Perspective
par: Cao, Yuan-dong, et autres
Publié: (2026)

Physics-Informed Neural Networks and Extensions
par: Raissi, Maziar, et autres
Publié: (2024)

Masked Autoencoder with Swin Transformer Network for Mitigating Electrode Shift in HD-EMG-based Gesture Recognition
par: Laamerad, Kasra, et autres
Publié: (2024)

An Analytic Solution to Covariance Propagation in Neural Networks
par: Wright, Oren, et autres
Publié: (2024)

PIMSM: Physics-Informed Multi-Scale Mamba for Stable Neural Representations under Distribution Shift
par: Bae, Sangyoon, et autres
Publié: (2026)

Physics-Guided Transformer (PGT): Physics-Aware Attention Mechanism for PINNs
par: Zeraatkar, Ehsan, et autres
Publié: (2026)

Towards Scalable and Deep Graph Neural Networks via Noise Masking
par: Liang, Yuxuan, et autres
Publié: (2024)

Physics-Informed Graph Neural Networks for Water Distribution Systems
par: Ashraf, Inaam, et autres
Publié: (2024)

Improved Training of Physics-Informed Neural Networks with Model Ensembles
par: Haitsiukevich, Katsiaryna, et autres
Publié: (2022)

Lightweight Geometric Adaptation for Training Physics-Informed Neural Networks
par: An, Kang, et autres
Publié: (2026)

Perception-Informed Neural Networks: Beyond Physics-Informed Neural Networks
par: Mazandarani, Mehran, et autres
Publié: (2025)

Evidential Physics-Informed Neural Networks
par: Tan, Hai Siong, et autres
Publié: (2025)

Graph Data Augmentation with Contrastive Learning on Covariate Distribution Shift
par: Zeng, Fanlong, et autres
Publié: (2025)

Bridging Multicalibration and Out-of-distribution Generalization Beyond Covariate Shift
par: Wu, Jiayun, et autres
Publié: (2024)

Physics-Informed Neural Network Surrogate Models for River Stage Prediction
par: Zoch, Maximilian, et autres
Publié: (2025)

Exploring Physics-Informed Neural Networks for Crop Yield Loss Forecasting
par: Miranda, Miro, et autres
Publié: (2024)

Physics-Informed Neural Networks with Learnable Loss Balancing and Transfer Learning
par: Pirayeshshirazinezhad, Reza
Publié: (2026)

Algorithmic Fairness Generalization under Covariate and Dependence Shifts Simultaneously
par: Zhao, Chen, et autres
Publié: (2023)

Enhancing material behavior discovery using embedding-oriented Physically-Guided Neural Networks with Internal Variables
par: Muñoz-Sierra, Rubén, et autres
Publié: (2025)

Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift
par: Yu, Tianrun, et autres
Publié: (2025)

Score-based Conditional Out-of-Distribution Augmentation for Graph Covariate Shift
par: Wang, Bohan, et autres
Publié: (2024)

DiffGrad for Physics-Informed Neural Networks
par: Rahman, Jamshaid Ul, et autres
Publié: (2024)

NeuroPhysNet: A FitzHugh-Nagumo-Based Physics-Informed Neural Network Framework for Electroencephalograph (EEG) Analysis and Motor Imagery Classification
par: Xia, Zhenyu, et autres
Publié: (2025)

Gradient Routing: Masking Gradients to Localize Computation in Neural Networks
par: Cloud, Alex, et autres
Publié: (2024)

iPINNER: An Iterative Physics-Informed Neural Network with Ensemble Kalman Filter
par: Lu, Binghang, et autres
Publié: (2025)

Limitations of Physics-Informed Neural Networks: a Study on Smart Grid Surrogation
par: Cestero, Julen, et autres
Publié: (2025)

Deep Neural Networks as Discrete Dynamical Systems: Implications for Physics-Informed Learning
par: Ganguly, Abhisek, et autres
Publié: (2026)

Drug Release Modeling using Physics-Informed Neural Networks
par: Qureshi, Daanish Aleem, et autres
Publié: (2026)

CITRAS: Covariate-Informed Transformer for Time Series Forecasting
par: Yamaguchi, Yosuke, et autres
Publié: (2025)