[HTML][HTML] Integral-scale validation of the SCIANTIX code for Light Water Reactor fuel rods
Mechanistic multi-scale modelling holds the potential to inform fuel performance codes by
incorporating high-fidelity models, algorithms, parameters, and material properties. In this …
incorporating high-fidelity models, algorithms, parameters, and material properties. In this …
Machine Learning with Physics Knowledge for Prediction: A Survey
This survey examines the broad suite of methods and models for combining machine
learning with physics knowledge for prediction and forecast, with a focus on partial …
learning with physics knowledge for prediction and forecast, with a focus on partial …
Towards Solving Industry-Grade Surrogate Modeling Problems using Physics Informed Machine Learning
S Bhatnagar, A Comerford, A Banaeizadeh - arXiv preprint arXiv …, 2023 - arxiv.org
Deep learning combined with physics-based modeling represents an attractive and efficient
approach for producing accurate and robust surrogate modeling. In this paper, a new …
approach for producing accurate and robust surrogate modeling. In this paper, a new …
Physics Informed Neural Networks for Modeling of 3D Flow-Thermal Problems with Sparse Domain Data
S Bhatnagar, A Comerford… - Journal of Machine …, 2024 - dl.begellhouse.com
ABSTRACT Successfully training Physics Informed Neural Networks (PINNs) for highly
nonlinear PDEs on complex 3D domains remains a challenging task. In this paper, PINNs …
nonlinear PDEs on complex 3D domains remains a challenging task. In this paper, PINNs …
A Bayesian framework for in-flight calibration and discrepancy reduction of spacecraft operational simulation models
F Antonello, D Segneri, J Eggleston - Advances in Space Research, 2024 - Elsevier
Abstract Modeling and Simulation (M&S) tools have become indispensable for the
comprehensive design, operations, and maintenance of products in the space industry. An …
comprehensive design, operations, and maintenance of products in the space industry. An …
Surrogate model-based calibration of a flying Earth observation satellite
F Antonello, D Segneri, V Reggestad - Advances in Space Research, 2024 - Elsevier
Abstract At the European Space Agency (ESA), Modeling and Simulation (M&S) plays a
fundamental role during the lifetime of a spacecraft, being used from the design phase to the …
fundamental role during the lifetime of a spacecraft, being used from the design phase to the …
Fast prediction of key parameters in FEBA using the COSINE subchannel code and artificial neural network
Y Guo, H Zhang, L Chen, M Zhao, Y Yang - Nuclear Engineering and …, 2024 - Elsevier
Numerical techniques have emerged as an essential tool for operators and designers to
preemptively acquire key parameters in accidents analysis. However, due to insufficient …
preemptively acquire key parameters in accidents analysis. However, due to insufficient …
[HTML][HTML] A recurrent neural network for modeling natural circulation density wave instabilities
P Hurley, JP Duarte - Nuclear Engineering and Technology, 2024 - Elsevier
Advancements in machine learning and deep learning capabilities over several decades
have resulted in models specialized for unique data structures and use cases. One such …
have resulted in models specialized for unique data structures and use cases. One such …
[HTML][HTML] Integration of artificial intelligence within an advanced filtering framework for real-time system state estimation and risk prediction with application to a nuclear …
Novel nuclear reactor designs, such as Small Modular Reactors and Microreactors, require
advanced safety assessment methods to analyze potential threats and hazards, and …
advanced safety assessment methods to analyze potential threats and hazards, and …
核电蒸汽系统数字孪生模型自动化同步技术.
刘浩， 肖云龙， 肖焱山， 曾祥云… - Science Technology & …, 2024 - search.ebscohost.com
摘要核电数字孪生模型与实际机组的同步运行, 是核电安全运行的重要保证.
以核电蒸汽系统的数字孪生模型为研究对象, 通过5 层全连接神经网络实现蒸汽系统代理模型的 …
以核电蒸汽系统的数字孪生模型为研究对象, 通过5 层全连接神经网络实现蒸汽系统代理模型的 …