The paper defines interpretability as model structural transparency and explainability as scientific content mapping, discusses their trade-offs, and frames both as deliberate modeling choices for ML in physics.
title Machine-learning approaches to accelerating lattice simulations
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Reviews approaches such as Lefschetz thimbles, complex Langevin dynamics, dual variables, tensor renormalization group, and machine learning to control the sign problem in lattice field theories.
citing papers explorer
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Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physics
The paper defines interpretability as model structural transparency and explainability as scientific content mapping, discusses their trade-offs, and frames both as deliberate modeling choices for ML in physics.
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Lattice field theories with a sign problem
Reviews approaches such as Lefschetz thimbles, complex Langevin dynamics, dual variables, tensor renormalization group, and machine learning to control the sign problem in lattice field theories.