9 papers · 1 filter
Toward Practical Forecasts of Public Sentiments via Convexification for Mean Field Games: Evidence from Real World COVID-19 Discussion Data
Shi Chen, Michael V. Klibanov, Kevin McGoff +3
We apply a convexification-based numerical method to forecast public sentiment dynamics using Mean Field Games (MFGs). The theoretical foundation for the convexification approach,…
Global Convergence and Uniqueness for an Inverse Problem Posed by Gelfand
Michael V. Klibanov, Jingzhi Li, Tian Niu +1
The first globally convergent numerical method is developed for a coefficient inverse problem (CIP) for the d, wave equation with the unknown potential in the most ch…
Convexification Numerical Method for Imaging of Moving Targets
Michael V. Klibanov, Jingzhi Li, Vladimir G. Romanov +1
The problem of imaging of a moving target is formulated as a Coefficient Inverse Problem for a hyperbolic equation with its coefficient depending on all three spatial variables and…
Forecasting Public Sentiments via Mean Field Games
Michael V. Klibanov, Kevin McGoff, Trung Truong
Motivated by the goal of forecasting public sentiments, we consider a forecasting problem in the context of the Mean Field Games theory. We develop a numerical method, which is a v…
The Carleman Contraction Mapping Method for a Coefficient Inverse Problem of the Epidemiology
Michael V. Klibanov, Trung Truong
It is proposed to monitor spatial and temporal spreads of epidemics via solution of a Coefficient Inverse Problem for a system of three coupled nonlinear parabolic equations. To so…
Convexification With the Viscocity Term for Electrical Impedance Tomography
Michael V. Klibanov, Jingzhi Li, Zhipeng Yang
A version of the globally convergent convexification numerical method is constructed for the problem of Electrical Impedance Tomography in the 2D case. An important element of this…