Data Driven Based Method for Field Information Sensing

Acquiring the field information on temperature, pressure, concentration, or velocity is crucial for the monitoring of chemical reactors, multiphase flow systems, heat transfer units, atmospheric pollutants diffusion, and underground pollutant migration. In this paper, a dimensionality reduction matrix completion (DRMC) method is proposed for the field information sensing (FIS) of objects of interest from the scattered point measurement data. An objective functional that casts the FIS task as an optimization problem is proposed. An iteration scheme is developed for solving the proposed objective functional. Numerical simulations are implemented to validate the feasibility and effectiveness of the proposed algorithm. It is found that differing from common inverse problems, numerical simulation approaches, and tomography based field measurement methods, in the proposed method the field information can be reconstructed without the knowledge on governing equations of the measurement objects, initial conditions, boundary conditions, and physical properties of materials, except the limited number of the measurement data. As a result, an alternative insight is introduced for the FIS problems.