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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
Mobility-based prediction of SARS-CoV-2 spreading
This paper analyzes the effectiveness of containment measures for SARS-CoV-2, using mobility data to gauge their impact. A deep learning model predicts virus spread scenarios in Italy, showing how these measures help flatten the infection curve and estimating the time required for their noticeable effects.
Lorenzo Chicchi, Lorenzo Giambagli, Lorenzo Buffoni, Duccio Fanelli
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