# Physics-informed Autoencoders for Lyapunov-stable Fluid Flow Prediction

@article{Erichson2019PhysicsinformedAF, title={Physics-informed Autoencoders for Lyapunov-stable Fluid Flow Prediction}, author={N. Benjamin Erichson and Michael Muehlebach and Michael W. Mahoney}, journal={ArXiv}, year={2019}, volume={abs/1905.10866} }

In addition to providing high-profile successes in computer vision and natural language processing, neural networks also provide an emerging set of techniques for scientific problems. Such data-driven models, however, typically ignore physical insights from the scientific system under consideration. Among other things, a physics-informed model formulation should encode some degree of stability or robustness or well-conditioning (in that a small change of the input will not lead to drastic…

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