mlsanityData to 14 Oct 2026

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

arXiv 2308.07523 ↗First posted 15 Aug 2023stat.ML cs.LG stat.COcited by 22

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Abstract (arXiv)

This paper focuses on the feasibility of Deep Neural Operator (DeepONet) as a robust surrogate modeling method within the context of digital twin (DT) for nuclear energy systems. Through benchmarking and evaluation, this study showcases the generalizability and computational efficiency of DeepONet in solving a challenging particle transport problem. DeepONet also exhibits remarkable prediction accuracy and speed, outperforming traditional ML methods, making it a suitable algorithm for real-time DT inference. However, the application of DeepONet also reveals challenges related to optimal sensor placement and model evaluation, critical aspects of real-world implementation. Addressing these challenges will further enhance the method's practicality and reliability. Overall, DeepONet presents a promising and transformative nuclear engineering research and applications tool. Its accurate prediction and computational efficiency capabilities can revolutionize DT systems, advancing nuclear engineering research. This study marks an important step towards harnessing the power of surrogate modeling techniques in critical engineering domains.

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). Builds on 9 of its 29 references that are in the map. First posted on arXiv 15 Aug 2023.

Builds on

9 of 29 references.

References · 9

  1. 2019DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operatorscited by 2,018
  2. 2019DeepXDE: A deep learning library for solving differential equationscited by 619
  3. 2021A physics-informed variational DeepONet for predicting the crack path in brittle materialscited by 98
  4. 2023Explainable, Interpretable & Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Lifecited by 19
  5. 2023Improved generalization with deep neural operators for engineering systems: Path towards digital twincited by 19
  6. 2022Leveraging Industry 4.0 -- Deep Learning, Surrogate Model and Transfer Learning with Uncertainty Quantification Incorporated into Digital Twin for Nuclear Systemcited by 7
  7. 2022Prediction and Uncertainty Quantification of SAFARI-1 Axial Neutron Flux Profiles with Neural Networkscited by 6
  8. 2022Digital Twin-Centered Hybrid Data-Driven Multi-Stage Deep Learning Framework for Enhanced Nuclear Reactor Power Predictioncited by 6
  9. 2022Digital Twin and Artificial Intelligence Incorporated With Surrogate Modeling for Hybrid and Sustainable Energy Systemscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 11; 2026: 10.

22 papers.

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  1. 2024Virtual Sensing-Enabled Digital Twin Framework for Real-Time Monitoring of Nuclear Systems Leveraging Deep Neural Operatorscited by 12
  2. 2025Geometry-Informed Neural Operator Transformercited by 8
  3. 2025Structure-Preserving Digital Twins via Conditional Neural Whitney Formscited by 8
  4. 2025From Proxies to Fields: Spatiotemporal Reconstruction of Global Radiation from Sparse Sensor Sequencescited by 7
  5. 2025Physics-Informed Time-Integrated DeepONet: Temporal Tangent Space Operator Learning for High-Accuracy Inferencecited by 5
  6. 2026CTF4Nuclear: Common Task Framework for Nuclear Fission and Fusion Modelscited by 5
  7. 2026Real-Time Sensing of Inaccessible Physical Fields via an Edge-Deployable Hardware-Portable Graph Neural Operatorcited by 4
  8. 2025Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operatorscited by 3
  9. 2025Constrained Sensing and Reliable State Estimation with Shallow Recurrent Decoders on a TRIGA Mark II Reactorcited by 3
  10. 2025Deep Neural ODE Operator Networks for PDEscited by 3
  11. 2025Agentic Physical AI toward a Domain-Specific Foundation Model for Energy Systems: A Case Study on Nuclear Reactor Controlcited by 3
  12. 2025Single vs. Multiple Branches in DeepONet and S-DeepONet: Network Architecture Follows Coupling in Multiphysics Systemscited by 2
10 more
  1. 2026Beyond Uniform Sampling: Synergistic Active Learning and Input Denoising for Robust Neural Operatorscited by 2
  2. 2025Artificial Intelligence in Reactor Physics: Current Status and Future Prospectscited by 1
  3. 2026SCNO: Spiking Compositional Neural Operator -- Towards a Neuromorphic Foundation Model for Nuclear PDE Solvingcited by 1
  4. 2026Neuromorphic Continual Learning for Sequential Deployment of Nuclear Plant Monitoring Systemscited by 1
  5. 2025Sensing Without Colocation: Operator-Based Virtual Instrumentation for Domains Beyond Physical Reachcited by 0
  6. 2026Validation-Gated Multi-Agent Governance for Online Adaptation of Thermal-Hydraulic Surrogate Models under Operating-Regime Shiftcited by 0
  7. 2026Neural Operator Surrogates for Two-Dimensional Neutron Flux Estimationcited by 0
  8. 2026Learning to Resolve Neutron Resonances with Fully Convolutional Neural Networkscited by 0
  9. 2026Adapting neural operators for mechanics decisions under changing operating conditionscited by 0
  10. 2026PTNO: Training Neural Operators with Noisy Monte Carlo Estimates for Particle Transport Problemscited by 0