Cover Image

Improved Training of Physics-Informed Neural Networks with Model Ensembles

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Haitsiukevich, Katsiaryna, Ilin, Alexander
Format:	Preprint
Published:	2022
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Learning Trajectories of Hamiltonian Systems with Neural Networks
by: Haitsiukevich, Katsiaryna, et al.
Published: (2022)

Diffusion models as probabilistic neural operators for recovering unobserved states of dynamical systems
by: Haitsiukevich, Katsiaryna, et al.
Published: (2024)

iPINNER: An Iterative Physics-Informed Neural Network with Ensemble Kalman Filter
by: Lu, Binghang, et al.
Published: (2025)

Lightweight Geometric Adaptation for Training Physics-Informed Neural Networks
by: An, Kang, et al.
Published: (2026)

Information Hidden in Gradients of Regression with Target Noise
by: Jamshidi, Arash, et al.
Published: (2026)

Complex Physics-Informed Neural Network
by: Si, Chenhao, et al.
Published: (2025)

Physics-Informed Neural Networks and Extensions
by: Raissi, Maziar, et al.
Published: (2024)

Efficient Ensembles Improve Training Data Attribution
by: Deng, Junwei, et al.
Published: (2024)

Densely Multiplied Physics Informed Neural Networks
by: Jiang, Feilong, et al.
Published: (2024)

Physics-Informed Neural Network Surrogate Models for River Stage Prediction
by: Zoch, Maximilian, et al.
Published: (2025)

In-Context Symbolic Regression: Leveraging Large Language Models for Function Discovery
by: Merler, Matteo, et al.
Published: (2024)

Drug Release Modeling using Physics-Informed Neural Networks
by: Qureshi, Daanish Aleem, et al.
Published: (2026)

Modeling COVID-19 Dynamics in German States Using Physics-Informed Neural Networks
by: Rothenbeck, Phillip, et al.
Published: (2025)

Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control
by: Amer, Abdelhakim, et al.
Published: (2025)

Physics-Informed Graph Neural Networks for Water Distribution Systems
by: Ashraf, Inaam, et al.
Published: (2024)

Emergence of Structure in Ensembles of Random Neural Networks
by: Muscarnera, Luca, et al.
Published: (2025)

Perception-Informed Neural Networks: Beyond Physics-Informed Neural Networks
by: Mazandarani, Mehran, et al.
Published: (2025)

Improving Line Search Methods for Large Scale Neural Network Training
by: Kenneweg, Philip, et al.
Published: (2024)

Evidential Physics-Informed Neural Networks
by: Tan, Hai Siong, et al.
Published: (2025)

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks
by: Elaarabi, Mouad, et al.
Published: (2025)

Exploring Physics-Informed Neural Networks for Crop Yield Loss Forecasting
by: Miranda, Miro, et al.
Published: (2024)

GINN-KAN: Interpretability pipelining with applications in Physics Informed Neural Networks
by: Ranasinghe, Nisal, et al.
Published: (2024)

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

Weight Ensembling Improves Reasoning in Language Models
by: Dang, Xingyu, et al.
Published: (2025)

Improved Uncertainty Quantification in Physics-Informed Neural Networks Using Error Bounds and Solution Bundles
by: Flores, Pablo, et al.
Published: (2025)

LION-DG: Layer-Informed Initialization with Deep Gradient Protocols for Accelerated Neural Network Training
by: Kim, Hyunjun
Published: (2026)

DiffGrad for Physics-Informed Neural Networks
by: Rahman, Jamshaid Ul, et al.
Published: (2024)

Physics-Informed Neural Networks with Architectural Physics Embedding for Large-Scale Wave Field Reconstruction
by: Zhang, Huiwen, et al.
Published: (2026)

Mask-PINNs: Mitigating Internal Covariate Shift in Physics-Informed Neural Networks
by: Jiang, Feilong, et al.
Published: (2025)

Limitations of Physics-Informed Neural Networks: a Study on Smart Grid Surrogation
by: Cestero, Julen, et al.
Published: (2025)

Deep Neural Networks as Discrete Dynamical Systems: Implications for Physics-Informed Learning
by: Ganguly, Abhisek, et al.
Published: (2026)

Rethinking Spiking Neural Networks from an Ensemble Learning Perspective
by: Ding, Yongqi, et al.
Published: (2025)

SeqBattNet: A Discrete-State Physics-Informed Neural Network with Aging Adaptation for Battery Modeling
by: Tran, Khoa, et al.
Published: (2025)

Transfer learning in Scalable Graph Neural Network for Improved Physical Simulation
by: Shen, Siqi, et al.
Published: (2025)

Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting
by: Song, Hongjin, et al.
Published: (2025)

naPINN: Noise-Adaptive Physics-Informed Neural Networks for Recovering Physics from Corrupted Measurement
by: Kim, Hankyeol, et al.
Published: (2026)

Meta-Inverse Physics-Informed Neural Networks for High-Dimensional Ordinary Differential Equations
by: Wei, Zhao, et al.
Published: (2026)

Rethinking Input Domains in Physics-Informed Neural Networks via Geometric Compactification Mappings
by: Huang, Zhenzhen, et al.
Published: (2026)

MP-PINN: A Multi-Phase Physics-Informed Neural Network for Epidemic Forecasting
by: Nguyen, Thang, et al.
Published: (2024)

SnapE -- Training Snapshot Ensembles of Link Prediction Models
by: Shaban, Ali, et al.
Published: (2024)