Computational Heat Transfer and Data-Driven Inverse Modelling Using Neural Networks (Deep Learning)

Deep learning approaches are used to study the feasibility of solving inverse problems with linear and non-linear behaviour. The boundary conditions in inverse issues are defined by sparse measurements of a variable such as velocity or temperature. Although this is mathematically tractable for basic issues, complex problems can be tremendously difficult. To address the non-linear and complicated effects, a brute force technique was utilised to get an approximate solution through trial and error. Machine learning techniques may now make it possible to model inverse situations more quickly and accurately. We propose a synthesis of computational mechanics and machine learning to illustrate that machine learning can be utilised to solve inverse problems. To establish a database, the forward problems must be solved first. The machine learning algorithms are then trained using this database. From assumed measurements, the trained algorithm is utilised to establish the problem's boundary conditions.

Author(s): Banting Pearson

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