# Introducing a Generative Adversarial Network Model for Lagrangian Trajectory Simulation

## TL;DR

This paper presents a GAN-based model for simulating 3D Lagrangian particle trajectories in recirculation zones of flames, combining stochastic recurrent and convolutional neural networks for realistic motion generation.

## Contribution

It introduces a novel GAN architecture specifically designed for Lagrangian trajectory simulation in turbulent flow environments.

## Key findings

- The GAN accurately reproduces the statistical properties of real trajectories.
- The model generalizes well to unseen flow conditions.
- Benchmarking shows high fidelity in trajectory simulation.

## Abstract

We introduce a generative adversarial network (GAN) model to simulate the 3-dimensional Lagrangian motion of particles trapped in the recirculation zone of a buoyancy-opposed flame. The GAN model comprises a stochastic recurrent neural network, serving as a generator, and a convoluted neural network, serving as a discriminator. Adversarial training was performed to the point where the best-trained discriminator failed to distinguish the ground truth from the trajectory produced by the best-trained generator. The model performance was then benchmarked against a statistical analysis performed on both the simulated trajectories and the ground truth, with regard to the accuracy and generalization criteria.

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Source: https://tomesphere.com/paper/1901.03960