10 citations · 17 across the 13 of their papers we have counts for
17 papers
Frozen in Time: How a Physics Foundation Model Forgets Under Sim-to-Real Adaptation
Haaris Mian, Miles Cranmer, Alessandro Favero +1
Scientific foundation models (SciFMs) have achieved remarkable performance on a wide range of PDEs, including the ability to efficiently adapt to unseen data distributions with add…
From Simulated Fluids to Living Tissue: Zero-Shot Foundation Model Transfer to Perturbed Embryos
Louisa Cornelis, Haewon Jeong, Irina Espejo +1
Morphogenesis is driven by the interplay of genetic regulation and tissue mechanics. In vivo video data is limited, particularly under genetic mutations and environmental perturbat…
Mesh-Native Tokenisation: Retrofitting Foundation Models to Unstructured Data
Georg Maierhofer, Miles Cranmer, Payel Mukhopadhyay
Physics foundation models are an increasingly popular approach for building versatile emulators for spatiotemporal systems. They require tokenisation layers that lift physical fiel…
Otter Weather: Skillful and Computationally Efficient Medium-Range Weather Forecasting
Cristiana Diaconu, Jonas Scholz, Aliaksandra Shysheya +4
State-of-the-art medium-range AI weather models can outperform traditional Numerical Weather Prediction (NWP) but require massive training budgets. This restricts usage for under-r…
Emergent Transfer of a Physics Foundation Model from Simulation to Laboratory Turbulence
Payel Mukhopadhyay, Stefan S. Nixon, Romain Watteaux +20
Whether physics foundation models can be usefully deployed on laboratory experiments remains an open question for scientific machine learning (ML). We test this question on the Ray…
MIMIC: A Generative Multimodal Foundation Model for Biomolecules
Siavash Golkar, Jake Kovalic, Irina Espejo Morales +28
Biological function emerges from coupled constraints across sequence, structure, regulation, evolution, and cellular context, yet most foundation models in biology are trained with…
On the Value of Tokeniser Pretraining in Physics Foundation Models
Hadi Sotoudeh, Payel Mukhopadhyay, Ruben Ohana +4
We investigate the impact of tokeniser pretraining on the accuracy and efficiency of physics emulation. Modern high-resolution simulations produce vast volumes of data spanning div…
Probabilistic Retrofitting of Learned Simulators
Cristiana Diaconu, Miles Cranmer, Richard E. Turner +2
Dominant approaches for modelling Partial Differential Equations (PDEs) rely on deterministic predictions, yet many physical systems of interest are inherently chaotic and uncertai…
Physics Steering: Causal Control of Cross-Domain Concepts in a Physics Foundation Model
Rio Alexa Fear, Payel Mukhopadhyay, Michael McCabe +2
Foundation models trained on text and images have been shown to develop abstract internal features that align with human concepts and that can be directly manipulated via activatio…
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…
Angle-dependent in-situ fast flavor transformations in post-neutron star merger disks
Kelsey A. Lund, Payel Mukhopadhyay, Jonah M. Miller +1
The remnant black hole-accretion disk system resulting from binary neutron star mergers has proven to be a promising site for synthesizing the heaviest elements via rapid neutron c…
The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning
Ruben Ohana, Michael McCabe, Lucas Meyer +24
Machine learning based surrogate models offer researchers powerful tools for accelerating simulation-based workflows. However, as standard datasets in this space often cover small…
The time evolution of fast flavor crossings in post-merger disks around a black hole remnant
Payel Mukhopadhyay, Jonah Miller, Gail C. McLaughlin
We postprocess a three-dimensional general relativistic, full transport neutrino radiation magnetohydrodynamics simulation of the black hole--accretion disk--wind system thought to…