Deep Neural Operator Driven Real Time Inference for Nuclear Systems to
Enable Digital Twin Solutions

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Deep Neural Operator Driven Real Time Inference for Nuclear Systems to Enable Digital Twin Solutions

15 August 2023

Kazuma Kobayashi

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Papers citing "Deep Neural Operator Driven Real Time Inference for Nuclear Systems to Enable Digital Twin Solutions"

5 / 5 papers shown

Title
Prediction and Uncertainty Quantification of SAFARI-1 Axial Neutron Flux Profiles with Neural Networks L. Moloko P. Bokov Xu Wu K. Ivanov 26 9 0 16 Nov 2022
Leveraging Industry 4.0 -- Deep Learning, Surrogate Model and Transfer Learning with Uncertainty Quantification Incorporated into Digital Twin for Nuclear System M. Rahman Abid Khan Sayeed Anowar Md. Al Imran Richa Verma Dinesh Kumar Kazuma Kobayashi S. B. Alam AI4CE 60 15 0 30 Sep 2022
A physics-informed variational DeepONet for predicting the crack path in brittle materials S. Goswami Minglang Yin Yue Yu G. Karniadakis AI4CE 54 196 0 16 Aug 2021
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators Lu Lu Pengzhan Jin George Karniadakis 248 2,158 0 08 Oct 2019
DeepXDE: A deep learning library for solving differential equations Lu Lu Xuhui Meng Zhiping Mao George Karniadakis PINN AI4CE 99 1,544 0 10 Jul 2019

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