4 citations · 7 across the 6 of their papers we have counts for
7 papers
PIKS: Universal Physics-Informed Kernel Methods
Joachim Bona-Pellissier, Giacomo Meanti, Matteo Santacesaria +1
Physics-informed machine learning incorporates physical principles --often expressed via differential operators-- into data-driven models. While physics-informed neural networks (P…
kooplearn: A Scikit-Learn Compatible Library of Algorithms for Evolution Operator Learning
Giacomo Turri, Grégoire Pacreau, Giacomo Meanti +8
kooplearn is a machine-learning library that implements linear, kernel, and deep-learning estimators of dynamical operators and their spectral decompositions. kooplearn can model b…
Fast and Fourier Features for Transfer Learning of Interatomic Potentials
Pietro Novelli, Giacomo Meanti, Pedro J. Buigues +4
Training machine learning interatomic potentials that are both computationally and data-efficient is a key challenge for enabling their routine use in atomistic simulations. To thi…
Physics Informed Shallow Machine Learning for Wind Speed Prediction
Daniele Lagomarsino-Oneto, Giacomo Meanti, Nicolò Pagliana +4
The ability to predict wind is crucial for both energy production and weather forecasting. Mechanistic models that form the basis of traditional forecasting perform poorly near the…
Multiclass learning with margin: exponential rates with no bias-variance trade-off
Stefano Vigogna, Giacomo Meanti, Ernesto De Vito +1
We study the behavior of error bounds for multiclass classification under suitable margin conditions. For a wide variety of methods we prove that the classification error under a h…
Efficient Hyperparameter Tuning for Large Scale Kernel Ridge Regression
Giacomo Meanti, Luigi Carratino, Ernesto De Vito +1
Kernel methods provide a principled approach to nonparametric learning. While their basic implementations scale poorly to large problems, recent advances showed that approximate so…