Sergei Manzhos

17 papers Journal 17

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Gaussian Process Regression - Neural Network Hybrid with Optimized Redundant Coordinates. CoRR Sergei Manzhos, Manabu Ihara
2025	J	`jnl`	Machine learning-guided construction of an analytic kinetic energy functional for orbital free density functional theory. Mach. Learn. Sci. Technol. Sergei Manzhos, Johann Lüder, Pavlo Golub, Manabu Ihara
2025	J	`jnl`	Neural networks for neurocomputing circuits: a computational study of tolerance to noise and activation function non-uniformity when machine learning materials properties. CoRR Ye min Thant, Methawee Nukunudompanich, Chu-Chen Chueh, Manabu Ihara, Sergei Manzhos
2023	J	`jnl`	Degeneration of kernel regression with Matern kernels into low-order polynomial regression in high dimension. CoRR Sergei Manzhos, Manabu Ihara
2023	J	`jnl`	Neural network with optimal neuron activation functions based on additive Gaussian process regression. CoRR Sergei Manzhos, Manabu Ihara
2023	J	`jnl`	Orders-of-coupling representation with a single neural network with optimal neuron activation functions and without nonlinear parameter optimization. CoRR Sergei Manzhos, Manabu Ihara
2022	J	`jnl`	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. Sergei Manzhos, Eita Sasaki, Manabu Ihara
2022	J	`jnl`	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. Owen Ren, Mohamed Ali Boussaidi, Dmitry A. Voytsekhovsky, Manabu Ihara, Sergei Manzhos
2022	J	`jnl`	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 Sergei Manzhos, Manabu Ihara
2021	J	`jnl`	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 Eita Sasaki, Manabu Ihara, Sergei Manzhos
2021	J	`jnl`	On the optimization of hyperparameters in Gaussian process regression. CoRR Sergei Manzhos, Manabu Ihara
2021	J	`jnl`	Rectangularization of Gaussian process regression for optimization of hyperparameters. CoRR Sergei Manzhos, Manabu Ihara
2020	J	`jnl`	CONUNDrum: A program for orbital-free density functional theory calculations. Comput. Phys. Commun. Pavlo Golub, Sergei Manzhos
2020	J	`jnl`	Machine learning for the solution of the Schrödinger equation. Mach. Learn. Sci. Technol. Sergei Manzhos
2015	J	`jnl`	A Comparative Density Functional Theory and Density Functional Tight Binding Study of Phases of Nitrogen Including a High Energy Density Material N Comput. Nicholas Capel, Devang Bharania, Sergei Manzhos
2013	J	`jnl`	Effect of Isotopic Substitution on Elementary Processes in Dye-Sensitized Solar Cells: Deuterated Amino-Phenyl Acid Dyes on TiO Comput. Sergei Manzhos, Hiroshi Segawa, Koichi Yamashita
2009	J	`jnl`	Fitting sparse multidimensional data with low-dimensional terms. Comput. Phys. Commun. Sergei Manzhos, Koichi Yamashita, Tucker Carrington Jr.