Imagen de Portada

Neuroscience Inspired Graph Operators Towards Edge-Deployable Virtual Sensing for Irregular Geometries

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Howes, William, Ahmed, Farid, Kobayashi, Kazuma, Chakraborty, Souvik, Alam, Syed Bahauddin
Formato:	Preprint
Publicado:	2026
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Real-Time Sensing of Inaccessible Physical Fields via an Edge-Deployable Hardware-Portable Graph Neural Operator
por: Howes, William, et al.
Publicado: (2026)

Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators
por: Kobayashi, Kazuma, et al.
Publicado: (2025)

Virtual Sensing to Enable Real-Time Monitoring of Inaccessible Locations \& Unmeasurable Parameters
por: Kobayashi, Kazuma, et al.
Publicado: (2024)

Sensing Without Colocation: Operator-Based Virtual Instrumentation for Domains Beyond Physical Reach
por: Yoo, Jay Phil, et al.
Publicado: (2025)

Deep Neural Operator Driven Real Time Inference for Nuclear Systems to Enable Digital Twin Solutions
por: Kobayashi, Kazuma, et al.
Publicado: (2023)

Virtual Sensing-Enabled Digital Twin Framework for Real-Time Monitoring of Nuclear Systems Leveraging Deep Neural Operators
por: Hossain, Raisa Bentay, et al.
Publicado: (2024)

Adversarial Vulnerabilities in Neural Operator Digital Twins: Gradient-Free Attacks on Nuclear Thermal-Hydraulic Surrogates
por: Roy, Samrendra, et al.
Publicado: (2026)

Explainable, Interpretable & Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Life
por: Kobayashi, Kazuma, et al.
Publicado: (2023)

Beyond Uniform Sampling: Synergistic Active Learning and Input Denoising for Robust Neural Operators
por: Roy, Samrendra, et al.
Publicado: (2026)

Gradient-Free Continual Learning in Spiking Neural Networks via Inter-Spike Interval Regularization
por: Roy, Samrendra, et al.
Publicado: (2026)

SCNO: Spiking Compositional Neural Operator -- Towards a Neuromorphic Foundation Model for Nuclear PDE Solving
por: Roy, Samrendra, et al.
Publicado: (2026)

Improved generalization with deep neural operators for engineering systems: Path towards digital twin
por: Kobayashi, Kazuma, et al.
Publicado: (2023)

When Spike Sparsity Does Not Translate to Deployed Cost: VS-WNO on Jetson Orin Nano
por: Yoo, Jason, et al.
Publicado: (2026)

TrustFed: Enabling Trustworthy Medical AI under Data Privacy Constraints
por: Kumar, Vagish, et al.
Publicado: (2026)

Digital Twin-Centered Hybrid Data-Driven Multi-Stage Deep Learning Framework for Enhanced Nuclear Reactor Power Prediction
por: Daniell, James, et al.
Publicado: (2022)

When Network Architecture Meets Physics: Deep Operator Learning for Coupled Multiphysics
por: Kobayashi, Kazuma, et al.
Publicado: (2025)

Agentic Physical AI toward a Domain-Specific Foundation Model for Energy Systems: A Case Study on Nuclear Reactor Control
por: Lee, Yoon Pyo, et al.
Publicado: (2025)

CoNBONet: Conformalized Neuroscience-inspired Bayesian Operator Network for Reliability Analysis
por: Garg, Shailesh, et al.
Publicado: (2026)

From Proxies to Fields: Spatiotemporal Reconstruction of Global Radiation from Sparse Sensor Sequences
por: Kobayashi, Kazuma, et al.
Publicado: (2025)

Neuromorphic Continual Learning for Sequential Deployment of Nuclear Plant Monitoring Systems
por: Roy, Samrendra, et al.
Publicado: (2026)

Q-SINDy: Quantum-Kernel Sparse Identification of Nonlinear Dynamics with Provable Coefficient Debiasing
por: Roy, Samrendra, et al.
Publicado: (2026)

Hardware-Software Co-Design for Event-Driven SNN Deployment on Low-Cost Neuromorphic FPGAs
por: Lee, Jiwoon, et al.
Publicado: (2026)

AI-driven non-intrusive uncertainty quantification of advanced nuclear fuels for digital twin-enabling technology
por: Kobayashi, Kazuma, et al.
Publicado: (2022)

Geometry adaptive waveformer for cardio-vascular modeling
por: N, Navaneeth, et al.
Publicado: (2025)

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions
por: Park, Jaewan, et al.
Publicado: (2025)

When Safety Geometry Collapses: Fine-Tuning Vulnerabilities in Agentic Guard Models
por: Hossain, Ismail, et al.
Publicado: (2026)

CortiNet: A Physics-Perception Hybrid Cortical-Inspired Dual-Stream Network for Gallbladder Disease Diagnosis from Ultrasound
por: Kumar, Vagish, et al.
Publicado: (2026)

From Local Interactions to Global Operators: Scalable Gaussian Process Operator for Physical Systems
por: Kumar, Sawan, et al.
Publicado: (2025)

Neural Operator induced Gaussian Process framework for probabilistic solution of parametric partial differential equations
por: Kumar, Sawan, et al.
Publicado: (2024)

The Art of the Jailbreak: Formulating Jailbreak Attacks for LLM Security Beyond Binary Scoring
por: Hossain, Ismail, et al.
Publicado: (2026)

Generative flow induced neural architecture search: Towards discovering optimal architecture in wavelet neural operator
por: Soin, Hartej, et al.
Publicado: (2024)

NeuroPINNs: Neuroscience Inspired Physics Informed Neural Networks
por: Garg, Shailesh, et al.
Publicado: (2025)

Towards Gaussian Process for operator learning: an uncertainty aware resolution independent operator learning algorithm for computational mechanics
por: Kumar, Sawan, et al.
Publicado: (2024)

Graph-Based Operator Learning from Limited Data on Irregular Domains
por: Li, Yile, et al.
Publicado: (2025)

A Review of Neuroscience-Inspired Machine Learning
por: Ororbia, Alexander, et al.
Publicado: (2024)

Scalable Random Wavelet Features: Efficient Non-Stationary Kernel Approximation with Convergence Guarantees
por: Kumar, Sawan, et al.
Publicado: (2026)

Competition is the key: A Game Theoretic Causal Discovery Approach
por: Roy, Amartya, et al.
Publicado: (2025)

Distribution free uncertainty quantification in neuroscience-inspired deep operators
por: Garg, Shailesh, et al.
Publicado: (2024)

Spatio-spectral graph neural operator for solving computational mechanics problems on irregular domain and unstructured grid
por: Sarkar, Subhankar, et al.
Publicado: (2024)

MD-NOMAD: Mixture density nonlinear manifold decoder for emulating stochastic differential equations and uncertainty propagation
por: Thakur, Akshay, et al.
Publicado: (2024)