Simulation free reliability analysis: A physics-informed deep learning based approach

TL;DR

This paper introduces a simulation-free, physics-informed deep learning framework for reliability analysis that directly learns from physical laws, eliminating the need for costly simulations and achieving high accuracy.

Contribution

It develops a novel physics-informed neural network approach for reliability analysis that bypasses traditional simulation-based data generation.

Findings

Achieves high accuracy in benchmark reliability problems

Eliminates the need for expensive simulation data

Successfully incorporates physical laws into the neural network

Abstract

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.