Onderzoeker
Corneel Casert
- Disciplines:Astronomie en ruimtewetenschappen, Andere fysica, Klassieke fysica, Elementaire deeltjesfysica en hoge-energie fysica
Affiliaties
- Vakgroep Fysica en Sterrenkunde (Departement)
Lid
Vanaf25 sep 2017 → Heden
Publicaties
1 - 10 van 11
- Training neural networks using Metropolis Monte Carlo and an adaptive variant(2022)
Auteurs: Stephen Whitelam, Viktor Selin, Ian Benlolo, Corneel Casert, Isaac Tamblyn
- Optical lattice experiments at unobserved conditions with generative adversarial deep learning(2021)
Auteurs: Corneel Casert, Kyle Mills, Tom Vieijra, Jan Ryckebusch, Isaac Tamblyn
- Dynamical large deviations of two-dimensional kinetically constrained models using a neural-network state ansatz(2021)
Auteurs: Corneel Casert, Tom Vieijra, Stephen Whitelam, Isaac Tamblyn
- Restricted Boltzmann machines for quantum states with non-abelian or anyonic symmetries(2020)
Auteurs: Tom Vieijra, Corneel Casert, Jannes Nys, Wesley De Neve, Jutho Haegeman, Jan Ryckebusch, Frank Verstraete
- The isospin and neutron-to-proton excess dependence of short-range correlations(2019)
Auteurs: Jan Ryckebusch, Wim Cosyn, Sam Stevens, Corneel Casert, Jannes Nys
Pagina's: 21 - 28 - Isospin composition of the high-momentum fluctuations in nuclei from asymptotic momentum distributions(2019)
Auteurs: Jan Ryckebusch, Wim Cosyn, Tom Vieijra, Corneel Casert
- Interpretable machine learning for inferring the phase boundaries in a nonequilibrium system(2019)
Auteurs: Corneel Casert, Tom Vieijra, Jannes Nys, Jan Ryckebusch
- Quantifying the role of inactive links in social networks(2018)
Auteurs: Andres Maria Belaza Vallejo, Jan Ryckebusch, Aaron Bramson, Corneel Casert, Kevin Hoefman, Koen Schoors, Milan van den Heuvel, Benjamin Vandermarliere, Hocine Cherifi, Luca Maria Aiello, et al.
Pagina's: 207 - 209 - Adversarial generation of mesoscale surfaces from small-scale chemical motifs
Auteurs: Kyle Mills, Corneel Casert, Isaac Tamblyn
Pagina's: 23158 - 23163 - Robust prediction of force chains in jammed solids using graph neural networks
Auteurs: Rituparno Mandal, Corneel Casert, Peter Sollich