Simulation free reliability analysis: A physics-informed deep learning based approach
This addresses the bottleneck of simulation costs in reliability analysis for engineering and scientific domains, though it builds on existing physics-informed neural network methods.
The paper tackles reliability analysis problems by eliminating the need for expensive simulations through a physics-informed neural network approach that learns directly from physical laws, achieving high accuracy on three benchmark problems.
This paper presents a simulation free framework for solving reliability analysis problems. The method proposed is rooted in a recently developed deep learning approach, referred to as the physics-informed neural network. The primary idea is to learn the neural network parameters directly from the physics of the problem. With this, the need for running simulation and generating data is completely eliminated. Additionally, the proposed approach also satisfies physical laws such as invariance properties and conservation laws associated with the problem. The proposed approach is used for solving three benchmark reliability analysis problems. Results obtained illustrates that the proposed approach is highly accurate. Moreover, the primary bottleneck of solving reliability analysis problems, i.e., running expensive simulations to generate data, is eliminated with this method.