 Neuronal diversity can improve machine learning for physics and beyondScientific Reports (Sci Rep), 2022 |
 Machine-Learning Non-Conservative Dynamics for New-Physics DetectionPhysical Review E (PRE), 2021 |
 Learning Hamiltonian dynamics by reservoir computerPhysical Review E (PRE), 2021 |
 Similarity-Based Equational Inference in PhysicsPhysical Review Research (Phys. Rev. Res.), 2021 |
 |
 Probabilistic Grammars for Equation DiscoveryKnowledge-Based Systems (KBS), 2020 |
 Forecasting Hamiltonian dynamics without canonical coordinatesNonlinear dynamics (Nonlinear Dyn.), 2020 |
 |
 |
 Renormalized Mutual Information for Artificial Scientific DiscoveryPhysical Review Letters (PRL), 2020 |
 Learning the Ising Model with Generative Neural NetworksPhysical Review Research (PRResearch), 2020 |
 AI Feynman: a Physics-Inspired Method for Symbolic RegressionScience Advances (Sci Adv), 2019 |
