10 citations · 17 across the 13 of their papers we have counts for
14 papers
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
Recent advances in mechanistic interpretability have revealed that large language models (LLMs) develop internal representations corresponding not only to concrete entities but als…