# Enhancing Dynamic Mode Decomposition Workflow with In-Situ Visualization and Data Compression

arXiv: https://arxiv.org/abs/2208.07767 · HTML: https://mlsanity.com/paper/2208.07767/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 94 references that are in the map.
- First posted on arXiv 16 Aug 2022.

## Builds on (most cited first, 19 of 19)

- [Survey of multifidelity methods in uncertainty propagation, inference, and optimization](https://mlsanity.com/paper/1806.10761/) (2018)
- [Sparse identification of nonlinear dynamics for model predictive control in the low-data limit](https://mlsanity.com/paper/1711.05501/) (2017)
- [Dynamic mode decomposition with control](https://mlsanity.com/paper/1409.6358/) (2014)
- [A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder](https://mlsanity.com/paper/2009.11990/) (2020)
- [Understanding SSIM](https://mlsanity.com/paper/2006.13846/) (2020)
- [Bagging, optimized dynamic mode decomposition (BOP-DMD) for robust, stable forecasting with spatial and temporal uncertainty-quantification](https://mlsanity.com/paper/2107.10878/) (2021)
- [gLaSDI: Parametric Physics-informed Greedy Latent Space Dynamics Identification](https://mlsanity.com/paper/2204.12005/) (2022)
- [Online dynamic mode decomposition for time-varying systems](https://mlsanity.com/paper/1707.02876/) (2017)
- [Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques](https://mlsanity.com/paper/2107.11460/) (2021)
- [SDRBench: Scientific Data Reduction Benchmark for Lossy Compressors](https://mlsanity.com/paper/2101.03201/) (2021)
- [Efficient nonlinear manifold reduced order model](https://mlsanity.com/paper/2011.07727/) (2020)
- [Randomized Matrix Decompositions using R](https://mlsanity.com/paper/1608.02148/) (2016)
- [Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning](https://mlsanity.com/paper/2202.05460/) (2022)
- [MGARD+: Optimizing Multilevel Methods for Error-bounded Scientific Data Reduction](https://mlsanity.com/paper/2010.05872/) (2020)
- [An Incremental Approach to Online Dynamic Mode Decomposition for Time-Varying Systems with Applications to EEG Data Modeling](https://mlsanity.com/paper/1908.01047/) (2019)
- [Dynamic Mode Decomposition in Adaptive Mesh Refinement and Coarsening Simulations](https://mlsanity.com/paper/2104.14034/) (2021)
- [PyParSVD: A streaming, distributed and randomized singular-value-decomposition library](https://mlsanity.com/paper/2108.08845/) (2021)
- [Accelerating Parallel Write via Deeply Integrating Predictive Lossy Compression with HDF5](https://mlsanity.com/paper/2206.14761/) (2022)
- [Coupled and Uncoupled Dynamic Mode Decomposition in Multi-Compartmental Systems with Applications to Epidemiological and Additive Manufacturing Problems](https://mlsanity.com/paper/2110.06375/) (2021)

## Built on by (most cited first, 1 of 1)

- [Advances in Scientific Machine Learning for Coupled Fluid Flow and Transport](https://mlsanity.com/paper/2606.19562/) (2026)

## Abstract (arXiv)

Modern computational science and engineering applications are being improved by the advances in scientific machine learning. Data-driven methods such as Dynamic Mode Decomposition (DMD) can extract coherent structures from spatio-temporal data generated from dynamical systems and infer different scenarios for said systems. The spatio-temporal data comes as snapshots containing spatial information for each time instant. In modern engineering applications, the generation of high-dimensional snapshots can be time and/or resource-demanding. In the present study, we consider two strategies for enhancing DMD workflow in large numerical simulations: (i) snapshots compression to relieve disk pressure; (ii) the use of in situ visualization images to reconstruct the dynamics (or part of) in runtime. We evaluate our approaches with two 3D fluid dynamics simulations and consider DMD to reconstruct the solutions. Results reveal that snapshot compression considerably reduces the required disk space. We have observed that lossy compression reduces storage by almost 50% with low relative errors in the signal reconstructions and other quantities of interest. We also extend our analysis to data generated on-the-fly, using in-situ visualization tools to generate image files of our state vectors during runtime. On large simulations, the generation of snapshots may be slow enough to use batch algorithms for inference. Streaming DMD takes advantage of the incremental SVD algorithm and updates the modes with the arrival of each new snapshot. We use streaming DMD to reconstruct the dynamics from in-situ generated images. We show that this process is efficient, and the reconstructed dynamics are accurate.