184 citations · 283 across the 24 of their papers we have counts for
8 papers · 1 filter
Agentic Physical AI toward a Domain-Specific Foundation Model for Energy Systems: A Case Study on Nuclear Reactor Control
Yoon Pyo Lee, Samrendra Roy, Kazuma Kobayashi +5
The prevailing paradigm in AI for physical systems: scaling general-purpose foundation models toward universal multimodal reasoning, confronts a barrier at the control interface. F…
Sensing Without Colocation: Operator-Based Virtual Instrumentation for Domains Beyond Physical Reach
Jay Phil Yoo, Kazuma Kobayashi, Souvik Chakraborty +1
Classical sensing rests on one foundational assumption: the quantity of interest must be colocated with the measurement device. This is not an engineering convenience. It is the or…
Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions
Jaewan Park, Farid Ahmed, Kazuma Kobayashi +4
Video-diffusion models have recently set the standard in video generation, inpainting, and domain translation thanks to their training stability and high perceptual fidelity. Build…
Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators
Kazuma Kobayashi, Shailesh Garg, Farid Ahmed +2
Robust uncertainty quantification (UQ) remains a critical barrier to the safe deployment of deep learning in real-time virtual sensing, particularly in high-stakes domains where sp…
Single vs. Multiple Branches in DeepONet and S-DeepONet: Network Architecture Follows Coupling in Multiphysics Systems
Jaewan Park, Kazuma Kobayashi, Qibang Liu +3
`Real-time prediction of complex physical systems requires surrogate models that learn from data while representing strong multiphysics coupling. Deep Operator Networks have shown…
Spatiotemporal Reconstruction of Global Radiation from Sparse Sensor Sequences
Kazuma Kobayashi, Tapas Tripura, Jay Phil Yoo +5
Much of the physical world is inaccessible to direct measurement and can be reached only through the sparse traces it leaves in other observable variables. Here we introduce the Te…
Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel
Dinesh Kumar, Eralp Demir, Julio Spadotto +7
The mechanical properties and long-term structural reliability of crystalline materials are strongly influenced by microstructural features such as grain size, morphology, and crys…
AI-driven Uncertainty Quantification & Multi-Physics Approach to Evaluate Cladding Materials in a Microreactor
Alex Foutch, Kazuma Kobayashi, Ayodeji Alajo +2
The pursuit of enhanced nuclear safety has spurred the development of accident-tolerant cladding (ATC) materials for light water reactors (LWRs). This study investigates the potent…