1 citations · 1 across the 3 of their papers we have counts for
4 papers
Test-Time Forecasting: Recurrent Depth Enables In-Context Dynamics Prediction
Jeffrey B. Lai, John Quinn, Anthony Bao +1
Foundation models for dynamical systems can forecast unseen partial differential equations (PDEs), ordinary differential equations (ODEs), and time series during inference. However…
Transformers for dynamical systems learn transfer operators in-context
Anthony Bao, Jeffrey Lai, William Gilpin
Large-scale foundation models for scientific machine learning adapt to physical settings unseen during training, such as zero-shot transfer between turbulent scales. This phenomeno…
Universal Redundancies in Time Series Foundation Models
Anthony Bao, Venkata Hasith Vattikuti, Jeffrey Lai +1
Time Series Foundation Models (TSFMs) leverage extensive pretraining to accurately predict unseen time series during inference, without the need for task-specific fine-tuning. Thro…
Panda: A pretrained forecast model for chaotic dynamics
Jeffrey Lai, Anthony Bao, William Gilpin
Chaotic systems are intrinsically sensitive to small errors, challenging efforts to construct predictive data-driven models of real-world dynamical systems such as fluid flows or n…