1 citations · 4 across the 9 of their papers we have counts for
8 papers · 1 filter
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…
Fractal basins trap latent reasoning
Jeffrey Lai, Anthony Bao, John Quinn +1
Reasoning allows artificial intelligence models to revisit and correct their mistakes, enabling recent frontier advances in mathematical theorem solving, software engineering, and…
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…
Context parroting: A simple but tough-to-beat baseline for foundation models in scientific machine learning
Yuanzhao Zhang, William Gilpin
Recent time-series foundation models exhibit strong abilities to predict physical systems. These abilities include zero-shot forecasting, in which a model forecasts future states o…
Zero-shot forecasting of chaotic systems
Yuanzhao Zhang, William Gilpin
Time-series forecasting is a challenging problem that traditionally requires specialized models custom-trained for the specific task at hand. Recently, inspired by the success of l…
Generative learning for nonlinear dynamics
William Gilpin
Modern generative machine learning models demonstrate surprising ability to create realistic outputs far beyond their training data, such as photorealistic artwork, accurate protei…