Cover Image

RL-DAUNCE: Reinforcement Learning-Driven Data Assimilation with Uncertainty-Aware Constrained Ensembles

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Behnoudfar, Pouria, Chen, Nan
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Mathematical Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Bridging Idealized and Operational Models: An Explainable AI Framework for Earth System Emulators
by: Behnoudfar, Pouria, et al.
Published: (2025)

Accurate and Efficient Hybrid-Ensemble Atmospheric Data Assimilation in Latent Space with Uncertainty Quantification
by: Fan, Hang, et al.
Published: (2026)

Uncertainty Quantification for Machine Learning-Based Prediction: A Polynomial Chaos Expansion Approach for Joint Model and Input Uncertainty Propagation
by: Du, Xiaoping
Published: (2025)

A new Uncertainty Principle in Machine Learning
by: Dolotin, V., et al.
Published: (2026)

A review of graph neural network applications in mechanics-related domains
by: Zhao, Yingxue, et al.
Published: (2024)

U-Net Kalman Filter (UNetKF): An Example of Machine Learning-assisted Ensemble Data Assimilation
by: Lu, Feiyu
Published: (2024)

Symmetry Breaking in Neural Network Optimization: Insights from Input Dimension Expansion
by: Zhang, Jun-Jie, et al.
Published: (2024)

Fuxi-DA: A Generalized Deep Learning Data Assimilation Framework for Assimilating Satellite Observations
by: Xu, Xiaoze, et al.
Published: (2024)

A Learning-based Domain Decomposition Method
by: Wu, Rui, et al.
Published: (2025)

Small Ensemble-based Data Assimilation: A Machine Learning-Enhanced Data Assimilation Method with Limited Ensemble Size
by: Li, Zhilin, et al.
Published: (2025)

Learning Heat-based Equations in Self-similar variables
by: Wang, Shihao, et al.
Published: (2026)

Continual Learning as a Multiphase Moving-Boundary Problem
by: Khilar, Snigdha Chandan
Published: (2026)

Physics-Informed Deep Learning of Rate-and-State Fault Friction
by: Rucker, Cody, et al.
Published: (2023)

Learning Hamiltonian neural Koopman operator and simultaneously sustaining and discovering conservation law
by: Zhang, Jingdong, et al.
Published: (2024)

Reinforcement learning to maximise wind turbine energy generation
by: Soler, Daniel, et al.
Published: (2024)

Neumann Series-based Neural Operator for Solving Inverse Medium Problem
by: Liu, Ziyang, et al.
Published: (2024)

Solving Partial Differential Equations in Different Domains by Operator Learning method Based on Boundary Integral Equations
by: Meng, Bin, et al.
Published: (2024)

Data Assimilation in Chaotic Systems Using Deep Reinforcement Learning
by: Hammoud, Mohamad Abed El Rahman, et al.
Published: (2024)

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

Symbolic identification of tensor equations in multidimensional physical fields
by: Chen, Tianyi, et al.
Published: (2025)

Cooper: A Library for Constrained Optimization in Deep Learning
by: Gallego-Posada, Jose, et al.
Published: (2025)

CayleyPy RL: Pathfinding and Reinforcement Learning on Cayley Graphs
by: Chervov, A., et al.
Published: (2025)

Data Generation without Function Estimation
by: Daneshmand, Hadi, et al.
Published: (2025)

Support Vector Machine Kernels as Quantum Propagators
by: Kuo, Nan-Hong, et al.
Published: (2025)

Random-Matrix-Induced Simplicity Bias in Over-parameterized Variational Quantum Circuits
by: Qi, Jun, et al.
Published: (2026)

Ensemble forecasts in reproducing kernel Hilbert space family
by: Dufée, Benjamin, et al.
Published: (2022)

Distributional Reinforcement Learning on Path-dependent Options
by: Özsoy, Ahmet Umur
Published: (2025)

Exploring the Use of Machine Learning Weather Models in Data Assimilation
by: Tian, Xiaoxu, et al.
Published: (2024)

Physics-Informed Mixture Models and Surrogate Models for Precision Additive Manufacturing
by: Basterrech, Sebastian, et al.
Published: (2025)

Physics-informed neural networks to solve inverse problems in unbounded domains
by: Pérez-Bernal, Gregorio, et al.
Published: (2025)

Symbolic Neural Ordinary Differential Equations
by: Li, Xin, et al.
Published: (2025)

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling
by: Zang, Yaohua, et al.
Published: (2025)

PO-CKAN:Physics Informed Deep Operator Kolmogorov Arnold Networks with Chunk Rational Structure
by: Wu, Junyi, et al.
Published: (2025)

Frequency-adaptive tensor neural networks for high-dimensional multi-scale problems
by: Huang, Jizu, et al.
Published: (2025)

FEKAN: Feature-Enriched Kolmogorov-Arnold Networks
by: Menon, Sidharth S., et al.
Published: (2026)

Domain Knowledge Guided Bayesian Optimization For Autonomous Alignment Of Complex Scientific Instruments
by: Mishra, Aashwin, et al.
Published: (2026)

Efficient Error Certification for Physics-Informed Neural Networks
by: Eiras, Francisco, et al.
Published: (2023)

BEACON: Bayesian Experimental design Acceleration with Conditional Normalizing flows $-$ a case study in optimal monitor well placement for CO$_2$ sequestration
by: Orozco, Rafael, et al.
Published: (2024)

Loss Jump During Loss Switch in Solving PDEs with Neural Networks
by: Wang, Zhiwei, et al.
Published: (2024)

Generative Path-Finding Method for Wasserstein Gradient Flow
by: Liu, Chengyu, et al.
Published: (2026)