224 citations · 1k across the 30 of their papers we have counts for
32 papers
Stabilizing Autoregressive PDE Foundation Model Rollouts with Event-Triggered Context Healing
Jaewan Park, Jay Phil Yoo, Kazuma Kobayashi +5
Autoregressive PDE foundation models enable fast full-field forecasting but accumulate errors as predicted states are recursively reused. We introduce Event-Triggered Context Heali…
Disentangling Attention in Deep Operator Learning: A Controlled Study of Data-Driven and Physics-Informed Architectures
Amar Alem Koric, Qibang Liu, Seid Koric
Deep neural operators learn mappings between input functions and complete PDE solution fields, enabling forward evaluations of new problem instances orders of magnitude faster than…
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…
A comprehensive comparison of neural operators for 3D industry-scale engineering designs
Weiheng Zhong, Qibang Liu, Diab Abueidda +2
Neural operators have emerged as powerful tools for learning nonlinear mappings between function spaces, enabling real-time prediction of complex dynamics in diverse scientific and…
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…
Sequential Neural Operator Transformer for High-Fidelity Surrogates of Time-Dependent Non-linear Partial Differential Equations
Qibang Liu, Seid Koric
Partial differential equations (PDEs) are fundamental to modeling complex and nonlinear physical phenomena, but their numerical solution often requires significant computational re…