7 citations · 8 across the 9 of their papers we have counts for
11 papers
An axially symmetric stationary N-center solution of Einstein's vacuum equations
Aleksandr A. Shaideman, Jesus D. Arias H, Kirill V. Golubnichiy
Using the Euclidon method, a stationary solution of Einstein's vacuum equations was obtained, describing N rotating axially symmetric masses, which in the absence of rotation descr…
A Carleman Semi-Discrete Convexification Method Combined With Deep Learning for Electrical Impedance Tomography
Michael V. Klibanov, Kirill V. Golubnichiy, Benjamin Jiang
In this paper, a new semi-discrete version of the Carleman estimate-based convexification globally convergent numerical method is developed. It is used for the delivery of the star…
Corruption via Mean Field Games
Michael V. Klibanov, Mikhail Yu. Kokurin, Kirill V. Golubnichiy
A new mathematical model governing the development of a corrupted hierarchy is derived. This model is based on the Mean Field Games theory. A retrospective problem for that model i…
Stationary multiple euclidon solutions to the vacuum Einstein equations
Aleksandr A. Shaideman, Kirill V. Golubnichiy
The non-linear superposition of the stationary euclidon solution with an arbitrary axially symmetric stationary gravitational field on the basis of the method of variation of param…
Optimizing Stock Option Forecasting with the Assembly of Machine Learning Models and Improved Trading Strategies
Zheng Cao, Raymond Guo, Wenyu Du +2
This paper introduced key aspects of applying Machine Learning (ML) models, improved trading strategies, and the Quasi-Reversibility Method (QRM) to optimize stock option forecasti…
Solving the Stock Option Forecast problem by a numerical method for the Black-Scholes Equation with Machine Learning Classification Model
Benjamin Jiang, Matthieu Durieux, Kirill V. Golubnichiy
We proposed classification models that utilize the result from the Quasi-Reversibility Method, which solves the Black-Scholes equation to forecast the option prices one day in adva…
Application of Convolutional Neural Networks with Quasi-Reversibility Method Results for Option Forecasting
Zheng Cao, Wenyu Du, Kirill V. Golubnichiy
This paper presents a novel way to apply mathematical finance and machine learning (ML) to forecast stock options prices. Following results from the paper Quasi-Reversibility Metho…
Forecasting Stock Options Prices via the Solution of an Ill-Posed Problem for the Black-Scholes Equation
Michael V. Klibanov, Aleksander A. Shananin, Kirill V. Golubnichiy +1
In the previous paper (Inverse Problems, 32, 015010, 2016), a new heuristic mathematical model was proposed for accurate forecasting of prices of stock options for 1-2 trading days…
Application of Neural Network Machine Learning to Solution of Black-Scholes Equation
Mikhail V. Klibanov, Kirill V. Golubnichiy, Andrey V. Nikitin
This paper presents a novel way to predict options price for one day in advance, utilizing the method of Quasi-Reversibility for solving the Black-Scholes equation. The Black-Schol…
An Evaluation of novel method of Ill-Posed Problem for the Black-Scholes Equation solution
Kirill V. Golubnichiy, Tianyang Wang, Andrey V. Nikitin
It was proposed by Klibanov a new empirical mathematical method to work with the Black-Scholes equation. This equation is solved forwards in time to forecast prices of stock option…
Inverse problems for the nonlinear modified transfer equation
Kirill V. Golubnichiy
This article is devoted to inverse problems for nonlinear equations of the modified transfer, which can be regarded as a manageable problem. Various productions such problems for n…