Imagen de Portada

Towards Efficient Parametric State Estimation in Circulating Fuel Reactors with Shallow Recurrent Decoder Networks

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Riva, Stefano, Introini, Carolina, Kutz, J. Nathan, Cammi, Antonio
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Computational Engineering, Finance, and Science Computational Physics
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Constrained Sensing and Reliable State Estimation with Shallow Recurrent Decoders on a TRIGA Mark II Reactor
por: Riva, Stefano, et al.
Publicado: (2025)

Robust State Estimation from Partial Out-Core Measurements with Shallow Recurrent Decoder for Nuclear Reactors
por: Riva, Stefano, et al.
Publicado: (2024)

From Models To Experiments: Shallow Recurrent Decoder Networks on the DYNASTY Experimental Facility
por: Riva, Stefano, et al.
Publicado: (2025)

CTF4Nuclear: Common Task Framework for Nuclear Fission and Fusion Models
por: Riva, Stefano, et al.
Publicado: (2026)

PySHRED: A Python package for SHallow REcurrent Decoding for sparse sensing, model reduction and scientific discovery
por: Ye, David, et al.
Publicado: (2025)

UQ-SHRED: uncertainty quantification of shallow recurrent decoder networks for sparse sensing via engression
por: Gao, Mars Liyao, et al.
Publicado: (2026)

Toward AI-Driven Digital Twins for Metropolitan Floods: A Conditional Latent Dynamics Network Surrogate of the Shallow Water Equations
por: Si, Phillip, et al.
Publicado: (2026)

Towards Foundation Model for Chemical Reactor Modeling: Meta-Learning with Physics-Informed Adaptation
por: Wang, Zihao, et al.
Publicado: (2024)

Toward Developing Machine-Learning-Aided Tools for the Thermomechanical Monitoring of Nuclear Reactor Components
por: Machado, Luiz Aldeia, et al.
Publicado: (2025)

Machine Learning-Assisted Discovery of Flow Reactor Designs
por: Savage, Tom, et al.
Publicado: (2023)

Multi-objective Combinatorial Methodology for Nuclear Reactor Site Assessment: A Case Study for the United States
por: Erdem, Omer, et al.
Publicado: (2024)

SENDAI: A Hierarchical Sparse-measurement, EfficieNt Data AssImilation Framework
por: Zhang, Xingyue, et al.
Publicado: (2026)

An Interpretable Data-Driven Model of the Flight Dynamics of Hawks
por: France, Lydia, et al.
Publicado: (2026)

Decoding RWA Tokenized U.S. Treasuries: Functional Dissection and Address Role Inference
por: Luo, Junliang, et al.
Publicado: (2025)

Finite Operator Learning: Bridging Neural Operators and Numerical Methods for Efficient Parametric Solution and Optimization of PDEs
por: Rezaei, Shahed, et al.
Publicado: (2024)

Multi-Objective Reinforcement Learning-based Approach for Pressurized Water Reactor Optimization
por: Seurin, Paul, et al.
Publicado: (2023)

A Taxonomy of Numerical Differentiation Methods
por: Komarov, Pavel, et al.
Publicado: (2025)

Input Convex Lipschitz Recurrent Neural Networks for Robust and Efficient Process Modeling and Optimization
por: Wang, Zihao, et al.
Publicado: (2024)

Common Task Framework For a Critical Evaluation of Scientific Machine Learning Algorithms
por: Wyder, Philippe Martin, et al.
Publicado: (2025)

VENI, VINDy, VICI: a generative reduced-order modeling framework with uncertainty quantification
por: Conti, Paolo, et al.
Publicado: (2024)

Parametric Nonlinear Volterra Series via Machine Learning: Transonic Aerodynamics
por: Immordino, Gabriele, et al.
Publicado: (2024)

Superstructure Optimization with Embedded Neural Networks for Sustainable Aviation Fuel Production
por: Klimek, Alexander, et al.
Publicado: (2025)

Cheap2Rich: A Multi-Fidelity Framework for Data Assimilation and System Identification of Multiscale Physics -- Rotating Detonation Engines
por: Bao, Yuxuan, et al.
Publicado: (2026)

Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations
por: Behroozi, Abdolmehdi, et al.
Publicado: (2025)

Gaussian Process Surrogate Models for Efficient Estimation of Structural Response Distributions and Order Statistics
por: Flovik, Vegard, et al.
Publicado: (2025)

A Neural Operator Emulator for Coastal and Riverine Shallow Water Dynamics
por: Rivera-Casillas, Peter, et al.
Publicado: (2025)

Multiphysics Continuous Shape Optimization of the TAP Reactor Components
por: Altahhan, Muhammad Ramzy, et al.
Publicado: (2025)

Towards a Real-Time Simulation of Elastoplastic Deformation Using Multi-Task Neural Networks
por: Schmeitz, Ruben, et al.
Publicado: (2024)

Recurrent Stochastic Configuration Networks with Hybrid Regularization for Nonlinear Dynamics Modelling
por: Dang, Gang, et al.
Publicado: (2024)

Efficient Aircraft Design Optimization Using Multi-Fidelity Models and Multi-fidelity Physics Informed Neural Networks
por: Sarker, Apurba
Publicado: (2024)

Modeling Market States with Clustering and State Machines
por: Oliva, Christian, et al.
Publicado: (2025)

Brain-Inspired Physics-Informed Neural Networks: Bare-Minimum Neural Architectures for PDE Solvers
por: Markidis, Stefano
Publicado: (2024)

Deep Clustering based Boundary-Decoder Net for Inter and Intra Layer Stress Prediction of Heterogeneous Integrated IC Chip
por: Lim, Kart Leong, et al.
Publicado: (2026)

A Machine Learning-Fueled Modelfluid for Flowsheet Optimization
por: Bubel, Martin, et al.
Publicado: (2025)

Online Fine-Tuning of Carbon Emission Predictions using Real-Time Recurrent Learning for State Space Models
por: Lemmel, Julian, et al.
Publicado: (2025)

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming
por: Liu, Xiaofeng, et al.
Publicado: (2025)

A Machine Learning Approach For Bitcoin Forecasting
por: Sossi-Rojas, Stefano, et al.
Publicado: (2025)

Thermodynamically Consistent Latent Dynamics Identification for Parametric Systems
por: He, Xiaolong, et al.
Publicado: (2025)

Using Large Language Models for Parametric Shape Optimization
por: Zhang, Xinxin, et al.
Publicado: (2024)

Echo State Networks for Bitcoin Time Series Prediction
por: Sharma, Mansi, et al.
Publicado: (2025)