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

arXiv: https://arxiv.org/abs/2005.01302 · HTML: https://mlsanity.com/paper/2005.01302/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2020.

## Built on by (most cited first, 8 of 8)

- [GrADE: A graph based data-driven solver for time-dependent nonlinear partial differential equations](https://mlsanity.com/paper/2108.10639/) (2021)
- [Physics-integrated hybrid framework for model form error identification in nonlinear dynamical systems](https://mlsanity.com/paper/2109.00538/) (2021)
- [Machine learning based digital twin for stochastic nonlinear multi-degree of freedom dynamical system](https://mlsanity.com/paper/2103.15636/) (2021)
- [Surrogate assisted active subspace and active subspace assisted surrogate -- A new paradigm for high dimensional structural reliability analysis](https://mlsanity.com/paper/2105.04979/) (2021)
- [Gated Linear Model induced U-net for surrogate modeling and uncertainty quantification](https://mlsanity.com/paper/2111.05123/) (2021)
- [Learning governing physics from output only measurements](https://mlsanity.com/paper/2208.05609/) (2022)
- [Harnessing physics-informed operators for high-dimensional reliability analysis problems](https://mlsanity.com/paper/2409.04708/) (2024)
- [Physics-Informed Neural Network-based Reliability Analysis of Buried Pipelines](https://mlsanity.com/paper/2511.11613/) (2025)

## Abstract (arXiv)

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.