TY - JOUR
TI - Data-driven inverse modelling through neural network (deep learning) and computational heat transfer
AU - Tamaddon-Jahromi, Hamid Reza
AU - Chakshu, Neeraj Kavan
AU - Sazonov, Igor
AU - Evans, Llion M.
AU - Thomas, Hywel
AU - Nithiarasu, Perumal
T2 - Computer Methods in Applied Mechanics and Engineering
AB - 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, convectionâ€“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.
DA - 2020/09/01/
PY - 2020
DO - 10.1016/j.cma.2020.113217
DP - ScienceDirect
VL - 369
SP - 113217
J2 - Computer Methods in Applied Mechanics and Engineering
LA - en
SN - 0045-7825
UR - https://www.sciencedirect.com/science/article/pii/S0045782520304023
Y2 - 2021/07/15/20:18:04
KW - Computational mechanics
KW - Heat conduction
KW - Heat convectionâ€“conduction
KW - Inverse modelling
KW - Machine learning
KW - Natural convection
ER -