  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.
