Training of Artificial Neural Network to Solve the Inverse Heat Conduction Problem

The accurate knowledge of Heat Transfer Coefficients (which shows the amount of heat exchanged between an object and its environment) is essential for the design of precise heat transfer operations. There are many numerical methods available (usually based on heuristics, like Genetic Algorithms, Particle Swarm Optimization, Fireworks Algorithm, etc.) for solving the Inverse Heat Conduction Problem, but these usually have a high computational demand. As an alternative, it is possible to design an Artificial Neural Network model efficiently predicting the shape of the HTC function based on the temperature signals recorded during real heat treatment processes. This paper presents a feed-forward network and examines various aspects (learning time, training loss, validation loss) of the model using three different hardware configurations (single CPU, single GPU, multiple GPUs).