Assessment of Machine Learning Methods for State-to-State Approach in Nonequilibrium Flow Simulations
L. Campoli, E. Kustova, and P. Maltseva
Mathematics 10(6), 928 (2022). DOI

UnDiFi-2D: An unstructured discontinuity fitting code for 2D grids
L. Campoli, A. Assonitis, M. Ciallella, R. Paciorri, A. Bonfiglioli, M. Ricchiuto
Computer Physics Communications 271, 108202 (2021). DOI

Models validation and code profiling in state-to-state simulations of shock heated air flows
L. Campoli, O. Kunova, E. Kustova, M. Melnik
Acta Astronautica 175, 493-509 (2020). DOI

Numerical investigation of viscous fingering phenomenon for raw field data
F. Bakharev, L. Campoli, A. Enin, S. Matveenko, Y. Petrova, S. Tikhomirov, A. Yakovlev
Transport in Porous Media 132(2), 443-464 (2020). DOI

KAPPA: Kinetic approach to physical processes in atmospheres library in C++
L. Campoli, G.P. Oblapenko, E.V. Kustova
Computer Physics Communications 236, 244-267 (2019). DOI

Unsteady shock‐fitting for unstructured grids
A. Bonfiglioli, R. Paciorri, L. Campoli
International Journal for Numerical Methods in Fluids 81(4), 245-261 (2016). DOI

Shock-fitting and predictor-corrector explicit ALE Residual Distribution
L. Campoli, P. Quemar, A. Bonfiglioli, M. Ricchiuto
In: Shock Fitting (pp. 113-129). Springer, Cham (2017).

Assessment of machine learning methods for state-to-state approaches
L. Campoli, E. Kustova, P. Maltseva
arXiv preprint arXiv:2104.01042 (2021).