Data-driven inverse modelling through neural network (deep learning) and computational heat transfer
| Autor: | Llion Evans, Igor Sazonov, Hywel Rhys Thomas, Neeraj Kavan Chakshu, Perumal Nithiarasu, H.R. Tamaddon-Jahromi |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
business.industry Computer science Mechanical Engineering Deep learning Computational Mechanics General Physics and Astronomy Inverse 010103 numerical & computational mathematics Inverse problem Trial and error 01 natural sciences Computer Science Applications Data-driven 010101 applied mathematics Mechanics of Materials Computational mechanics Boundary value problem Artificial intelligence 0101 mathematics business Algorithm |
| Popis: | In this work, the potential of carrying out inverse problems with linear and non-linear behaviour is investigated using deep learning methods. In inverse problems, the boundary conditions are determined using sparse measurement of a variable such as velocity or temperature. Although this is mathematically tractable for simple problems, it can be extremely challenging for complex problems. To overcome the non-linear and complex effects, a brute force approach was used on a trial and error basis to find an approximate solution. With the advent of machine learning algorithms it may now be possible to model inverse problems faster and more accurately. In order to demonstrate that machine learning can be used in solving inverse problems, we propose a fusion between computational mechanics and machine learning. The forward problems are solved first to create a database. This database is then used to train the machine learning algorithms. The trained algorithm is then used to determine the boundary conditions of a problem from assumed measurements. The proposed method is tested for the linear/non-linear heat conduction, convection–conduction, and natural convection problems in which the boundary conditions are determined by providing three, four, and five temperature measurements. This study demonstrates that the proposed fusion of computational mechanics and machine learning is an effective way of tackling complex inverse problems. |
| Databáze: | OpenAIRE |
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