Image de couverture de livre

Agentic Physical AI toward a Domain-Specific Foundation Model for Energy Systems: A Case Study on Nuclear Reactor Control

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Lee, Yoon Pyo, Roy, Samrendra, Kobayashi, Kazuma, Talukder, Sajedul, Abueidda, Diab, Koric, Seid, Chakraborty, Souvik, Alam, Syed Bahauddin
Format:	Preprint
Publié:	2025
Sujets:	Artificial Intelligence Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

From Proxies to Fields: Spatiotemporal Reconstruction of Global Radiation from Sparse Sensor Sequences
par: Kobayashi, Kazuma, et autres
Publié: (2025)

Gradient-Free Continual Learning in Spiking Neural Networks via Inter-Spike Interval Regularization
par: Roy, Samrendra, et autres
Publié: (2026)

Neuromorphic Continual Learning for Sequential Deployment of Nuclear Plant Monitoring Systems
par: Roy, Samrendra, et autres
Publié: (2026)

Virtual Sensing-Enabled Digital Twin Framework for Real-Time Monitoring of Nuclear Systems Leveraging Deep Neural Operators
par: Hossain, Raisa Bentay, et autres
Publié: (2024)

Adversarial Vulnerabilities in Neural Operator Digital Twins: Gradient-Free Attacks on Nuclear Thermal-Hydraulic Surrogates
par: Roy, Samrendra, et autres
Publié: (2026)

When Network Architecture Meets Physics: Deep Operator Learning for Coupled Multiphysics
par: Kobayashi, Kazuma, et autres
Publié: (2025)

SCNO: Spiking Compositional Neural Operator -- Towards a Neuromorphic Foundation Model for Nuclear PDE Solving
par: Roy, Samrendra, et autres
Publié: (2026)

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions
par: Park, Jaewan, et autres
Publié: (2025)

Beyond Uniform Sampling: Synergistic Active Learning and Input Denoising for Robust Neural Operators
par: Roy, Samrendra, et autres
Publié: (2026)

Q-SINDy: Quantum-Kernel Sparse Identification of Nonlinear Dynamics with Provable Coefficient Debiasing
par: Roy, Samrendra, et autres
Publié: (2026)

Deep Neural Operator Driven Real Time Inference for Nuclear Systems to Enable Digital Twin Solutions
par: Kobayashi, Kazuma, et autres
Publié: (2023)

Sensing Without Colocation: Operator-Based Virtual Instrumentation for Domains Beyond Physical Reach
par: Yoo, Jay Phil, et autres
Publié: (2025)

Digital Twin-Centered Hybrid Data-Driven Multi-Stage Deep Learning Framework for Enhanced Nuclear Reactor Power Prediction
par: Daniell, James, et autres
Publié: (2022)

Explainable, Interpretable & Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Life
par: Kobayashi, Kazuma, et autres
Publié: (2023)

Improved generalization with deep neural operators for engineering systems: Path towards digital twin
par: Kobayashi, Kazuma, et autres
Publié: (2023)

Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators
par: Kobayashi, Kazuma, et autres
Publié: (2025)

Neuroscience Inspired Graph Operators Towards Edge-Deployable Virtual Sensing for Irregular Geometries
par: Howes, William, et autres
Publié: (2026)

A comprehensive comparison of neural operators for 3D industry-scale engineering designs
par: Zhong, Weiheng, et autres
Publié: (2025)

Towards Signed Distance Function based Metamaterial Design: Neural Operator Transformer for Forward Prediction and Diffusion Model for Inverse Design
par: Liu, Qibang, et autres
Publié: (2025)

Geom-DeepONet: A Point-cloud-based Deep Operator Network for Field Predictions on 3D Parameterized Geometries
par: He, Junyan, et autres
Publié: (2024)

When Safety Geometry Collapses: Fine-Tuning Vulnerabilities in Agentic Guard Models
par: Hossain, Ismail, et autres
Publié: (2026)

SCALE: Self-regulated Clustered federAted LEarning in a Homogeneous Environment
par: Puppala, Sai, et autres
Publié: (2024)

Virtual Sensing to Enable Real-Time Monitoring of Inaccessible Locations \& Unmeasurable Parameters
par: Kobayashi, Kazuma, et autres
Publié: (2024)

AI-driven non-intrusive uncertainty quantification of advanced nuclear fuels for digital twin-enabling technology
par: Kobayashi, Kazuma, et autres
Publié: (2022)

TrustFed: Enabling Trustworthy Medical AI under Data Privacy Constraints
par: Kumar, Vagish, et autres
Publié: (2026)

Predictions of Transient Vector Solution Fields with Sequential Deep Operator Network
par: He, Junyan, et autres
Publié: (2023)

Material-Response-Informed DeepONet and its Application to Polycrystal Stress-strain Prediction in Crystal Plasticity
par: He, Junyan, et autres
Publié: (2024)

Geometry-Informed Neural Operator Transformer
par: Liu, Qibang, et autres
Publié: (2025)

Advanced Deep Operator Networks to Predict Multiphysics Solution Fields in Materials Processing and Additive Manufacturing
par: Kushwaha, Shashank, et autres
Publié: (2024)

The Art of the Jailbreak: Formulating Jailbreak Attacks for LLM Security Beyond Binary Scoring
par: Hossain, Ismail, et autres
Publié: (2026)

Real-Time Sensing of Inaccessible Physical Fields via an Edge-Deployable Hardware-Portable Graph Neural Operator
par: Howes, William, et autres
Publié: (2026)

Nonlinear Inverse Design of Mechanical Multi-Material Metamaterials Enabled by Video Denoising Diffusion and Structure Identifier
par: Park, Jaewan, et autres
Publié: (2024)

Semantic Intent Fragmentation: A Single-Shot Compositional Attack on Multi-Agent AI Pipelines
par: Ahad, Tanzim, et autres
Publié: (2026)

When Spike Sparsity Does Not Translate to Deployed Cost: VS-WNO on Jetson Orin Nano
par: Yoo, Jason, et autres
Publié: (2026)

Hardware-Software Co-Design for Event-Driven SNN Deployment on Low-Cost Neuromorphic FPGAs
par: Lee, Jiwoon, et autres
Publié: (2026)

Optimus-Q: Utilizing Federated Learning in Adaptive Robots for Intelligent Nuclear Power Plant Operations through Quantum Cryptography
par: Puppala, Sai, et autres
Publié: (2025)

Univariate Conditional Variational Autoencoder for Morphogenic Patterns Design in Frontal Polymerization-Based Manufacturing
par: Liu, Qibang, et autres
Publié: (2024)

Mechanistic Interpretability of LoRA-Adapted Language Models for Nuclear Reactor Safety Applications
par: Lee, Yoon Pyo
Publié: (2025)

Variational Gaussian Mixture Manifold Models for Client-Specific Federated Personalization
par: Puppala, Sai, et autres
Publié: (2025)

Agent-Fence: Mapping Security Vulnerabilities Across Deep Research Agents
par: Puppala, Sai, et autres
Publié: (2026)