16 citations · 30 across the 13 of their papers we have counts for
7 papers · 1 filter
Physics Steering: Causal Control of Cross-Domain Concepts in a Physics Foundation Model
Rio Alexa Fear, Payel Mukhopadhyay, Michael McCabe +2
Recent advances in mechanistic interpretability have revealed that large language models (LLMs) develop internal representations corresponding not only to concrete entities but als…
Predicting partially observable dynamical systems via diffusion models with a multiscale inference scheme
Rudy Morel, Francesco Pio Ramunno, Jeff Shen +18
Conditional diffusion models provide a natural framework for probabilistic prediction of dynamical systems and have been successfully applied to fluid dynamics and weather predicti…
Universal Spectral Tokenization via Self-Supervised Panchromatic Representation Learning
Jeff Shen, Francois Lanusse, Liam Holden Parker +24
Sequential scientific data span many resolutions and domains, and unifying them into a common representation is a key step toward developing foundation models for the sciences. Ast…
Walrus: A Cross-Domain Foundation Model for Continuum Dynamics
Michael McCabe, Payel Mukhopadhyay, Tanya Marwah +22
Foundation models have transformed machine learning for language and vision, but achieving comparable impact in physical simulation remains a challenge. Data heterogeneity and unst…
AION-1: Omnimodal Foundation Model for Astronomical Sciences
Liam Parker, Francois Lanusse, Jeff Shen +24
While foundation models have shown promise across a variety of fields, astronomy still lacks a unified framework for joint modeling across its highly diverse data modalities. In th…
Overtone: Cyclic Patch Modulation for Clean, Efficient, and Flexible Physics Emulators
Payel Mukhopadhyay, Michael McCabe, Ruben Ohana +1
Transformer-based PDE surrogates achieve remarkable performance but face two key challenges: fixed patch sizes cause systematic error accumulation at harmonic frequencies, and comp…
Lost in Latent Space: An Empirical Study of Latent Diffusion Models for Physics Emulation
François Rozet, Ruben Ohana, Michael McCabe +3
The steep computational cost of diffusion models at inference hinders their use as fast physics emulators. In the context of image and video generation, this computational drawback…