Symbolic regression produces an approximate classifier for LHC exclusion limits that enables their direct inclusion during pMSSM global fits.
Learning symbolic physics with graph networks
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COSINE jointly discovers latent interaction graphs and compact symbolic dynamical equations by using an LLM to iteratively prune and expand the function library based on optimization feedback.
Geometric deep learning provides a unified mathematical framework based on grids, groups, graphs, geodesics, and gauges to explain and extend neural network architectures by incorporating physical regularities.
citing papers explorer
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Symbolic Classification-Enabled LHC Limits Online BSM Global Fits
Symbolic regression produces an approximate classifier for LHC exclusion limits that enables their direct inclusion during pMSSM global fits.
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Interpretable Relational Inference with LLM-Guided Symbolic Dynamics Modeling
COSINE jointly discovers latent interaction graphs and compact symbolic dynamical equations by using an LLM to iteratively prune and expand the function library based on optimization feedback.
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Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
Geometric deep learning provides a unified mathematical framework based on grids, groups, graphs, geodesics, and gauges to explain and extend neural network architectures by incorporating physical regularities.