# Physics-informed dynamic mode decomposition (piDMD)

@article{Baddoo2021PhysicsinformedDM, title={Physics-informed dynamic mode decomposition (piDMD)}, author={Peter J. Baddoo and Benjamin Herrmann and Beverley J. McKeon and J. Nathan Kutz and Steven L. Brunton}, journal={ArXiv}, year={2021}, volume={abs/2112.04307} }

In this work, we demonstrate how physical principles – such as symmetries, invariances, and conservation laws – can be integrated into the dynamic mode decomposition (DMD). DMD is a widely-used data analysis technique that extracts low-rank modal structures and dynamics from high-dimensional measurements. However, DMD frequently produces models that are sensitive to noise, fail to generalize outside the training data, and violate basic physical laws. Our physics-informed DMD (piDMD…

## 3 Citations

Kernel learning for robust dynamic mode decomposition: linear and nonlinear disambiguation optimization

- Computer ScienceProceedings of the Royal Society A
- 2022

This work presents a kernel method that learns interpretable data-driven models for high-dimensional, nonlinear systems and shows that it is possible to recover the linear model contribution with this approach, thus separating the effects of the implicitly defined nonlinear terms.

Port-Hamiltonian Dynamic Mode Decomposition

- MathematicsArXiv
- 2022

. We present a novel physics-informed system identiﬁcation method to construct a passive linear time-invariant system. In more detail, for a given quadratic energy functional, measurements of the…

Residual Dynamic Mode Decomposition: Robust and verified Koopmanism

- Physics
- 2022

Dynamic Mode Decomposition (DMD) describes complex dynamic processes through a hierarchy of simpler coherent features. DMD is regularly used to understand the fundamental characteristics of…

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