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Sergei Manzhos
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2020 – today
- 2023
- [i7]Sergei Manzhos, Manabu Ihara:
Neural network with optimal neuron activation functions based on additive Gaussian process regression. CoRR abs/2301.05567 (2023) - [i6]Sergei Manzhos, Manabu Ihara:
Orders-of-coupling representation with a single neural network with optimal neuron activation functions and without nonlinear parameter optimization. CoRR abs/2302.12013 (2023) - [i5]Sergei Manzhos, Manabu Ihara:
Degeneration of kernel regression with Matern kernels into low-order polynomial regression in high dimension. CoRR abs/2311.10790 (2023) - 2022
- [j7]Owen Ren, Mohamed Ali Boussaidi, Dmitry A. Voytsekhovsky, Manabu Ihara, Sergei Manzhos:
Random Sampling High Dimensional Model Representation Gaussian Process Regression (RS-HDMR-GPR) for representing multidimensional functions with machine-learned lower-dimensional terms allowing insight with a general method. Comput. Phys. Commun. 271: 108220 (2022) - [j6]Sergei Manzhos, Eita Sasaki, Manabu Ihara:
Easy representation of multivariate functions with low-dimensional terms via Gaussian process regression kernel design: applications to machine learning of potential energy surfaces and kinetic energy densities from sparse data. Mach. Learn. Sci. Technol. 3(1): 01 (2022) - [i4]Sergei Manzhos, Manabu Ihara:
The loss of the property of locality of the kernel in high-dimensional Gaussian process regression on the example of the fitting of molecular potential energy surfaces. CoRR abs/2211.11170 (2022) - 2021
- [i3]Eita Sasaki, Manabu Ihara, Sergei Manzhos:
Easy representation of multivariate functions with low-dimensional terms via Gaussian process regression kernel design: applications to machine learning of potential energy surfaces and kinetic energy densities from sparse data. CoRR abs/2111.11001 (2021) - [i2]Sergei Manzhos, Manabu Ihara:
On the optimization of hyperparameters in Gaussian process regression. CoRR abs/2112.01374 (2021) - [i1]Sergei Manzhos, Manabu Ihara:
Rectangularization of Gaussian process regression for optimization of hyperparameters. CoRR abs/2112.02467 (2021) - 2020
- [j5]Pavlo Golub, Sergei Manzhos:
CONUNDrum: A program for orbital-free density functional theory calculations. Comput. Phys. Commun. 256: 107365 (2020) - [j4]Sergei Manzhos:
Machine learning for the solution of the Schrödinger equation. Mach. Learn. Sci. Technol. 1(1): 13002 (2020)
2010 – 2019
- 2015
- [j3]Nicholas Capel, Devang Bharania, Sergei Manzhos:
A Comparative Density Functional Theory and Density Functional Tight Binding Study of Phases of Nitrogen Including a High Energy Density Material N8. Comput. 3(4): 574-585 (2015) - 2013
- [j2]Sergei Manzhos, Hiroshi Segawa, Koichi Yamashita:
Effect of Isotopic Substitution on Elementary Processes in Dye-Sensitized Solar Cells: Deuterated Amino-Phenyl Acid Dyes on TiO2. Comput. 1(1): 1-15 (2013)
2000 – 2009
- 2009
- [j1]Sergei Manzhos, Koichi Yamashita, Tucker Carrington Jr.:
Fitting sparse multidimensional data with low-dimensional terms. Comput. Phys. Commun. 180(10): 2002-2012 (2009)
Coauthor Index
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