13 citations · 13 across the 5 of their papers we have counts for
6 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…
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…
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…
Nonlinear Inverse Design of Mechanical Multi-Material Metamaterials Enabled by Video Denoising Diffusion and Structure Identifier
Jaewan Park, Shashank Kushwaha, Junyan He +4
Metamaterials, synthetic materials with customized properties, have emerged as a promising field due to advancements in additive manufacturing. These materials derive unique mechan…
Advanced Deep Operator Networks to Predict Multiphysics Solution Fields in Materials Processing and Additive Manufacturing
Shashank Kushwaha, Jaewan Park, Seid Koric +3
Unlike classical artificial neural networks, which require retraining for each new set of parametric inputs, the Deep Operator Network (DeepONet), a lately introduced deep learning…
Predictions of Transient Vector Solution Fields with Sequential Deep Operator Network
Junyan He, Shashank Kushwaha, Jaewan Park +3
The Deep Operator Network (DeepONet) structure has shown great potential in approximating complex solution operators with low generalization errors. Recently, a sequential DeepONet…