AI4Physics Coffee Break
Alex ColagrandeExploring machine learning for physics, one coffee break at a time.
| Platform |  | Pricing | Only free issues | Publishes | Weekly |
|---|---|---|---|---|---|
| Issues | 17 | Founded | 10 months ago | Last Issue | 16 days ago |
| Active | |||||
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machinelearningatthelimit.substack....Latest Issues
17. Learning Physics by Warping Space
Displacement fields learned by Flower. Image taken from [1].
Introduction
In previous blog posts, we have explored several neural architectures for physics simulations, from Fourier Neural Operators to large Transformer-based foundation...
16. Pretraining Neural Simulators from Geometry only
GeoPT offers a way to scale up neural simulators with off-the-shelf geometries and enables fast fine-tuning for various physics. Image taken from [2].
Introduction
Recent deep learning research has shown that, to achieve outstanding res...
15. Inside the mind of a Foundation model for Physics
Introduction
For numerical foundation models the primary focus has been so far on predictive performance rather than understanding why and how the do what they do, leading them to be seen mostly as black boxes.
Understanding these syste...
14. Walrus: A Cross-Domain Foundation Model for Continuum Dynamics
Just a walrus, but not the Walrus we will be discussing today.
Introduction
We continue our trip in the wide jungle of foundation models for Physics and today we will explore Walrus, a 1.3B model jointly pretrained on 19 physical scenar...
13. Multi-branch transformers with AB-UPT
AB-UPT. Image taken from [1].
Introduction
This blog post is about multi-branch transformer architectures, we will discuss in details how in Anchor-Branched (AB) -Universal Physics Transformer (UPT), they designed a model that have diff...
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Authors
The writers behind this newsletter.
PhD student in ML4Physics at Paris Dauphine - PSL
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