# Deep Learning the Physics of Transport Phenomena

arXiv: https://arxiv.org/abs/1709.02432 · HTML: https://mlsanity.com/paper/1709.02432/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2017.

## Built on by (most cited first, 26 of 26)

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- [Heat Conduction Plate Layout Optimization using Physics-driven Convolutional Neural Networks](https://mlsanity.com/paper/2201.10002/) (2022)
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## Abstract (arXiv)

We have developed a new data-driven paradigm for the rapid inference, modeling and simulation of the physics of transport phenomena by deep learning. Using conditional generative adversarial networks (cGAN), we train models for the direct generation of solutions to steady state heat conduction and incompressible fluid flow purely on observation without knowledge of the underlying governing equations. Rather than using iterative numerical methods to approximate the solution of the constitutive equations, cGANs learn to directly generate the solutions to these phenomena, given arbitrary boundary conditions and domain, with high test accuracy (MAE<1%) and state-of-the-art computational performance. The cGAN framework can be used to learn causal models directly from experimental observations where the underlying physical model is complex or unknown.