Aldo Glielmo

Publications

• A Bayesian inference framework for compression and prediction of quantum states
• Gaussian Process States: A Data-Driven Representation of Quantum Many-Body Physics
• Unsupervised Learning Methods for Molecular Simulation Data
• Sponge: A generalized eigenproblem for clustering signed networks
• Accurate interatomic force fields via machine learning with covariant kernels
• On machine learning force fields for metallic nanoparticles
• Can we obtain the coefficient of restitution from the sound of a bouncing ball?
• Building machine learning force fields for nanoclusters
• Building Nonparametric n-Body Force Fields Using Gaussian Process Regression
• Enabling QM-accurate simulation of dislocation motion in γ-Ni and α-Fe using a hybrid multiscale approach
• Coefficient of restitution of aspherical particles
• Efficient nonparametric n -body force fields from machine learning
• Compact atomic descriptors enable accurate predictions via linear models
• Exploring the robust extrapolation of high-dimensional machine learning potentials
• Ranking the information content of distance measures
• DADApy: Distance-based analysis of data-manifolds in Python
• Black-it: A Ready-to-Use and Easy-to-Extend Calibration Kit for Agent-based Models
• Intrinsic Dimension Estimation for Discrete Metrics
• Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMs
• Divide-and-conquer potentials enable scalable and accurate predictions of forces and energies in atomistic systems
• Simulating the Economic Impact of Rationality through Reinforcement Learning and Agent-Based Modelling
• Assessing Inference to the Best Explanation Posteriors for the Estimation of Economic Agent-Based Models
• Assessing inference to the best explanation posteriors for the estimation of economic agent-based models
• Density Estimation via Binless Multidimensional Integration