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Peering inside the black box by learning the relevance of many-body functions in neural network potentials
This paper extend tools recently proposed in the nascent field of explainable artificial intelligence, such as Layerwise Relevance Propagation, to coarse-grained potentials based on graph neural networks.
Klara Bonneau, Jonas Lederer, Clark Templeton, David Rosenberger, Lorenzo Giambagli, Klaus-Robert Muller, Cecilia Clementi
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Peering inside the black box by learning the relevance of many-body functions in neural network potentials
Non-parametric analysis of the Hubble Diagram with Neural Networks
This study introduces a neural network-based method for nonparametric analysis of the Hubble diagram, extended to high redshifts. Validated using simulated data, the method aligns with a flat Λ (Lambda) cold dark matter model (ΩM ≈ 0.3) up to z ≈ 1-1.5, but deviates at higher redshifts. It also suggests increasing ΩM values with redshift, indicating potential dark energy evolution.
Lorenzo Giambagli, Duccio Fanelli, Guido Risaliti, Matilde Signorini
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Non-parametric analysis of the Hubble Diagram with Neural Networks