mcbal/spin-model-transformers

Physics-inspired transformer modules based on mean-field dynamics of vector-spin models in JAX

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Experimental

This project provides specialized building blocks for AI models that are inspired by how particles interact in physics. It takes in numerical data, like patterns or features, and transforms it using these unique interaction rules to produce enhanced or re-represented data. Scientists and researchers working with complex data patterns, especially those in physics-related fields, might use this to develop more robust machine learning models.

No commits in the last 6 months.

Use this if you are a researcher building advanced machine learning models and want to experiment with novel, physics-inspired architectures for processing complex data.

Not ideal if you are looking for an off-the-shelf solution for common data analysis tasks or are unfamiliar with developing custom neural network architectures.

physics-inspired AI scientific machine learning complex system modeling neural network research computational physics
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 8 / 25
Maturity 16 / 25
Community 5 / 25

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Stars

46

Forks

2

Language

Python

License

Apache-2.0

Last pushed

Dec 10, 2023

Commits (30d)

0

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