# A physics-informed GAN Framework based on Model-free Data-Driven Computational Mechanics

arXiv: https://arxiv.org/abs/2310.20308 · HTML: https://mlsanity.com/paper/2310.20308/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2023.

## Built on by (most cited first, 3 of 3)

- [Physics-informed diffusion models in spectral space](https://mlsanity.com/paper/2602.09708/) (2026)
- [Risk-Informed Diffusion Transformer for Long-Tail Trajectory Prediction in the Crash Scenario](https://mlsanity.com/paper/2501.16349/) (2025)
- [Bridging the Physics-Data Gap with FNO-Guided Conditional Flow Matching: Designing Inductive Bias through Hierarchical Physical Constraints](https://mlsanity.com/paper/2510.08295/) (2025)

## Abstract (arXiv)

Model-free data-driven computational mechanics, first proposed by Kirchdoerfer and Ortiz, replace phenomenological models with numerical simulations based on sample data sets in strain-stress space. In this study, we integrate this paradigm within physics-informed generative adversarial networks (GANs). We enhance the conventional physics-informed neural network framework by implementing the principles of data-driven computational mechanics into GANs. Specifically, the generator is informed by physical constraints, while the discriminator utilizes the closest strain-stress data to discern the authenticity of the generator's output. This combined approach presents a new formalism to harness data-driven mechanics and deep learning to simulate and predict mechanical behaviors.