Image de couverture de livre

Randomized Physics-Informed Neural Networks for Bayesian Data Assimilation

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Zong, Yifei, Barajas-Solano, David, Tartakovsky, Alexandre M.
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Mathematics of Digital Twins and Transfer Learning for PDE Models
par: Zong, Yifei, et autres
Publié: (2025)

Statistical Learning Analysis of Physics-Informed Neural Networks
par: Barajas-Solano, David A.
Publié: (2026)

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data
par: Zeng, Jice, et autres
Publié: (2024)

VAE-DNN: Energy-Efficient Trainable-by-Parts Surrogate Model For Parametric Partial Differential Equations
par: Zong, Yifei, et autres
Publié: (2025)

Transfer Learning on Multi-Dimensional Data: A Novel Approach to Neural Network-Based Surrogate Modeling
par: Propp, Adrienne M., et autres
Publié: (2024)

Total Uncertainty Quantification in Inverse PDE Solutions Obtained with Reduced-Order Deep Learning Surrogate Models
par: Wang, Yuanzhe, et autres
Publié: (2024)

Neural Incremental Data Assimilation
par: Blanke, Matthieu, et autres
Publié: (2024)

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems
par: Venkatasubramanian, Subashree, et autres
Publié: (2024)

Bayesian Reasoning for Physics Informed Neural Networks
par: Graczyk, Krzysztof M., et autres
Publié: (2023)

Bayesian Physics Informed Neural Networks for Reliable Transformer Prognostics
par: Ramirez, Ibai, et autres
Publié: (2025)

Sparsely-Supervised Data Assimilation via Physics-Informed Schrödinger Bridge
par: Bu, Dohyun, et autres
Publié: (2026)

Learning Hamiltonian Dynamics with Bayesian Data Assimilation
par: Kim, Taehyeun, et autres
Publié: (2025)

Bayesian Physics Informed Neural Networks for Linear Inverse problems
par: Mohammad-Djafari, Ali
Publié: (2025)

Data vs. Physics: The Apparent Pareto Front of Physics-Informed Neural Networks
par: Rohrhofer, Franz M., et autres
Publié: (2021)

$Φ$-DVAE: Physics-Informed Dynamical Variational Autoencoders for Unstructured Data Assimilation
par: Glyn-Davies, Alex, et autres
Publié: (2022)

Towards Scalable Bayesian Optimization via Gradient-Informed Bayesian Neural Networks
par: Makrygiorgos, Georgios, et autres
Publié: (2025)

Data Subsampling for Bayesian Neural Networks
par: Kawasaki, Eiji, et autres
Publié: (2022)

Generative AI-enhanced Probabilistic Multi-Fidelity Surrogate Modeling Via Transfer Learning
par: Zeng, Jice, et autres
Publié: (2026)

Causality and Bayesian network PDEs for multiscale representations of porous media
par: Um, Kimoon, et autres
Publié: (2019)

Physics-Informed Neural Networks for Dynamic Process Operations with Limited Physical Knowledge and Data
par: Velioglu, Mehmet, et autres
Publié: (2024)

Conformalized Physics-Informed Neural Networks
par: Podina, Lena, et autres
Publié: (2024)

Posterior Concentration of Bayesian Physics-Informed Neural Networks for Elliptic PDEs
par: Zhao, Yuxuan, et autres
Publié: (2026)

Uncertainty-Aware Prediction of Lung Tumor Growth from Sparse Longitudinal CT Data via Bayesian Physics-Informed Neural Networks
par: Kong, Lingfei, et autres
Publié: (2026)

On the Role of Consistency Between Physics and Data in Physics-Informed Neural Networks
par: Becerra-Zuniga, Nicolás, et autres
Publié: (2026)

Bayesian Physics-Informed Neural Networks for Inverse Problems (BPINN-IP): Application in Infrared Image Processing
par: Mohammad-Djafari, Ali, et autres
Publié: (2025)

Jacobian-Enforced Neural Networks (JENN) for Improved Data Assimilation Consistency in Dynamical Models
par: Tian, Xiaoxu
Publié: (2024)

FNP: Fourier Neural Processes for Arbitrary-Resolution Data Assimilation
par: Chen, Kun, et autres
Publié: (2024)

Randomized Geometric Algebra Methods for Convex Neural Networks
par: Wang, Yifei, et autres
Publié: (2024)

Deep Bayesian Filter for Bayes-faithful Data Assimilation
par: Tarumi, Yuta, et autres
Publié: (2024)

$PINN - a Domain Decomposition Method for Bayesian Physics-Informed Neural Networks
par: Figueres, Júlia Vicens, et autres
Publié: (2025)

Neural Dynamics-Informed Pre-trained Framework for Personalized Brain Functional Network Construction
par: Jiang, Hongjie, et autres
Publié: (2026)

Singular Bayesian Neural Networks
par: Toure, Mame Diarra, et autres
Publié: (2026)

Meta-Learning Fourier Neural Operators for Hessian Inversion and Enhanced Variational Data Assimilation
par: Moazzami, Hamidreza, et autres
Publié: (2025)

Guaranteeing Conservation Laws with Projection in Physics-Informed Neural Networks
par: Baez, Anthony, et autres
Publié: (2024)

Physics-Informed Neural Networks with Hard Nonlinear Equality and Inequality Constraints
par: Iftakher, Ashfaq, et autres
Publié: (2025)

Scalable Bayesian Physics-Informed Kolmogorov-Arnold Networks
par: Gao, Zhiwei, et autres
Publié: (2025)

Preconditioning for Physics-Informed Neural Networks
par: Liu, Songming, et autres
Publié: (2024)

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

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

Topological Approach for Data Assimilation
par: Chumley, Max M., et autres
Publié: (2024)