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| Issue | Title | |
| Vol 14, No 6 (2023) | Toward nanomagnetic implementation of energy-based machine learning | Abstract PDF (Eng) similar documents |
| I. S. Lobanov | ||
| "... Some approaches to machine learning (ML) such as Boltzmann machines (BM) can be reformulated ..." | ||
| Vol 5, No 5 (2014) | Few cycle pulses in the Bragg medium containing carbon nanotubes | Abstract PDF (Eng) similar documents |
| M. B. Belonenko, Ju. V. Nevzorova, E. MN. Galkina | ||
| Vol 8, No 6 (2017) | Kinetic coefficients of semiconductor superlattices in high-frequency electromagnetic fields | Abstract PDF (Eng) similar documents |
| A. V. Shorokhov, N. S. Prudskikh, M. B. Semenov, V. D. Krevchik, M. A. Pyataev, S. E. Golovatyuk, Tian-Rong Li, Yu-Hua Wang | ||
| "... Kinetic coefficients of semiconductor superlattice are obtained from the Boltzmann transport ..." | ||
| Vol 14, No 4 (2023) | The Space Charge Model. A new analytical approximation solution of Poisson– Boltzmann equation: the extended homogeneous approximation | Abstract PDF (Eng) similar documents |
| J. Dweik, H. Farhat, J. Younis | ||
| "... Poisson– Boltzmann (PB) equation based on a mean-field description of ions as ideal point charges ..." | ||
| Vol 11, No 6 (2020) | Machine learning method for computation of optimal transitions in magnetic nanosystems | Abstract PDF (Eng) similar documents |
| K. R. Bushuev, I. S. Lobanov | ||
| "... more effective methods are expected to exist. In this article, we propose a machine learning based ..." | ||
| Vol 7, No 6 (2016) | Minimum energy path calculations with Gaussian process regression | Abstract PDF (Eng) similar documents |
| O-P. Koistinen, E. Maras, H. Jonsson | ||
| Vol 16, No 5 (2025) | Bridging accuracy and efficiency: assessing universal ML potentials for niobiumoxygen clusters | Abstract PDF (Eng) similar documents |
| I. S. Popov, A. A. Valeeva, A. N. Enyashin | ||
| "... Machine Learning Interatomic Potentials (MLIPs) promise to combine the accuracy of DFT ..." | ||
| 1 - 7 of 7 Items | ||
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