Parallel Physics-Informed Neural Networks with Bidirectional Balance
This work addresses a specific challenge in engineering simulations for researchers and practitioners using PINNs, representing an incremental improvement.
The authors tackled the problem of physics-informed neural networks (PINNs) failing on partial differential equations (PDEs) with multiple coupled physical quantities and unbalanced variables, using heat transfer in multilayer fabrics as an example, and proposed a parallel framework with bidirectional balance that made previously unsolvable problems solvable and achieved excellent solving accuracy.
As an emerging technology in deep learning, physics-informed neural networks (PINNs) have been widely used to solve various partial differential equations (PDEs) in engineering. However, PDEs based on practical considerations contain multiple physical quantities and complex initial boundary conditions, thus PINNs often returns incorrect results. Here we take heat transfer problem in multilayer fabrics as a typical example. It is coupled by multiple temperature fields with strong correlation, and the values of variables are extremely unbalanced among different dimensions. We clarify the potential difficulties of solving such problems by classic PINNs, and propose a parallel physics-informed neural networks with bidirectional balance. In detail, our parallel solving framework synchronously fits coupled equations through several multilayer perceptions. Moreover, we design two modules to balance forward process of data and back-propagation process of loss gradient. This bidirectional balance not only enables the whole network to converge stably, but also helps to fully learn various physical conditions in PDEs. We provide a series of ablation experiments to verify the effectiveness of the proposed methods. The results show that our approach makes the PINNs unsolvable problem solvable, and achieves excellent solving accuracy.