Introducing a Generative Adversarial Network Model for Lagrangian Trajectory Simulation

Jingwei Gan; Pai Liu; Rajan K. Chakrabarty

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arXiv · 1901.03960

Introducing a Generative Adversarial Network Model for Lagrangian Trajectory Simulation

Jingwei Gan·Pai Liu·Rajan K. Chakrabarty

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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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BibTeX RIS

Jingwei Gan, Pai Liu, Rajan K. Chakrabarty. 2019-01-13. Introducing a Generative Adversarial Network Model for Lagrangian Trajectory Simulation. https://arxiv.org/abs/1901.03960

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