# Dynamic Mode Decomposition Fundamentals

# Fundamentals of Dynamic Mode Decomposition (DMD)

This post is incomplete, sorry! Hopefully I’ll get some time to come back and improve/revise this content.

There are many scholarly articles describing DMD more precisely than I can. However, I would like to offer my take from a more intuitive perspective. And I would like to emphasize a couple important points that caused confusion for me as I was trying to climb the learning curve.

Disclaimers! My degree is in computer science, so I often find myself playing catchup with grad school level engineering math. I work at the U of MN Aerospace Engineering Dept. as a full time researcher, but my comments here 100% reflect my own thoughts entirely. If I have said anything wildly incorrect or misleading here, please let me know so I can learn something and fix the issue!

## Overview

DMD simultaneously computes something like a Fourier series approximation to a set of input sensor signals. The input data could be a gridded time series from a fluid simulation, or perhaps a segment of video where each pixel is an independent sensor. The solution will share a common set of basis functions, and each sensor will have a unique set of weightings for each basis function. Just as with a Fourier series approximation, DMD can be used to reconstruct a lower order approximation to the input data set.

It is often useful to plot the weights for one of the basis functions to understand the how the “energy” of the corresponding frequency is structured in the system. Here is an arbitrary example of a mode plot:

##

## DMD Constraint #1: Sampling Time Interval

It only makes sense to perform DMD on a set of data if all the sensors have been sampled at a constant time interval. This is mentioned in the literature.

## DMD Constraint #2: Sensor/Sampling Locations

I have not found anywhere in the literature a stated requirement that the sensors must maintain a fixed position within the system. If sensors can move through the system during the experiment they will be sampling the system state at different locations. In all the DMD examples I have seen, there is an assumption the sensors have fixed locations within the experiment or system.

DMD will produce a result if the first constraint is met, but of the sensors are moving through the system, is that result meaningful or interpretable?

# Terminology

## Definition: Modes

Consider a system that has “n” sensors which are sampled at “m” time steps and we solve for “k” frequencies. DMD will compute a set of “n” weightings for each of the “k” basis functions (frequencies.) The frequencies can be evaluated along with phase/amplitude information at each of the “m” time steps.

We can refer to a set of “n” weightings associated with a single
frequency as a **mode.** Notice in the following plot the “x” axis is
each pixel position The vertical axis is the mode weights. In this
example the input data set is a 200 x 200 image (“n” = 40000 pixels)
and we solved for “k” = 9 basis functions.)

## Definition: Dynamics

We can evaluate the shared basis functions for the Fourier series at
each time step. This is called the **dynamics.**

The modes (the set of weightings) is fixed for the entire solution and the dynamics changes with respect to time. For the dynamics plot, the horizontal axis is time and the vertical axis is amplitude. Also note that in the input video for this plot, there was motion at the beginning, but very little motion at the end which corresponds to diminishing dynamics over time.)

## Definition: Pixel Motion

Consider a video clip with an object moving through the frame. Focus on a single pixel as the video plays through. It is important to distinguish between how DMD sees the scene and how our eyes/brain see the scene.

DMD is fitting a Fourier series to a time sequence of data points from individual pixels. As the bicycle rides across the scene, the individual pixels are changing values at specific times to create the illusion of motion. Our brains see motion, but pixels are not actually moving, and the values each pixel assumes over time don’t really convey velocity or direction information.

# Reconstructing the original sensor data

The DMD solution provides enough information to reconstruct an approximation to the original video at any time step.

At a conceptual level each pixel has a unique set of basis function weights and can be reconstructed individually, however in practice we would use block (matrix) operations to reconstruct an entire frame of video in one step.

To reconstruct an approximation at a specific time step, simply sum the product of each mode (set of weights) multiplied by the respective basis function evaluated at that time step.

The example below shows the original time series for a single pixel compared to the Fourier series approximation as computed by DMD. As stated above, the value of every pixel at every time step can be approximated. Thus it is possible to reconstruct (an approximation to) the entire input video using only the modes and the basis functions.

Notice the reconstructed fit for each individual sensor with DMD will not be as accurate as if a Fourier series was computed for each sensor independently due to the shared frequencies in the DMD solution.

Also notice the many “impulse” style changes in the original signal. These changes relate to the arbitrary motion of the camera and the edges of arbitrary areas of the scene passing by each specific pixel.

A large number of modes (basis functions) are required to closely approximate these sharp and unpredictable changes over the course of the video sement. This implies that there isn’t specific frequency information embedded in the signal beyond changing or not changing. The Fourier series needs “all” the frequencies to approximate these impulse changes. Notice also that in this example of a moving camera, the chosen pixel value is always changing.

# More About Modes

The modes (the set of sensor weightings for each basis function) can directly provide insight into the motion of the system. The modes can expose the complex structures forming the dynamics of the system. Another way to say this is that mode (weighting) shows how responsive each sensor is to each frequency.

## Visualizing Modes

It can be useful to plot modes to see the energy (weights) at different basis function frequencies. Remember the mode is just the set of weights for a specific Fourier basis function (frequency.)

Cherry picking an arbitrary mode from an arbitrary stationary video with an object moving through as an example, the mode plot could look like the following. This plot shows some energy at some particular frequency at some specific locations in the video frame. It shows zero energy in all the background (not changing) positions. In this case the X and Y axes are pixel locations in the original video. This allows us to “see” where energy at some frequency has occurred:

## Problem #1: Separating the amplitude of the input signals versus response at that frequency.

Remember that fundamentally DMD computes a Fourier series approximation to the original data set. We can use the output of DMD to reconstruct the original pixel values at any time “t”. Consider that some pixels values will be small (dark regions) and some pixel values will be large (light regions.) To properly reconstruct the original value, those dark pixels will have a low mode weighting, while the bright pixels have a much higher relative weighting. Thus, each mode (set of weights) is a mixture of the original signal amplitude and the response at a specific frequency. There is no way to directly separate if a low mode value (weighting) means a low response at that frequency, or the original pixel was just a dark pixel.

## Problem #2: Impulse changes (step changes)

Consider the following “video” (just one frame is shown.) An arbitrary pixel is selected and shown in the green circle. Full DMD is performed for the entire 7.7 second video clip using a maximum rank of 9 (9 basis functions for the Fourier series approximation.)

As the bike rides “through” the chosen pixel, here is the pixel value over time. Hopefully anyone that has seen demonstrations of using Fourier series to approximate step functions or square waves in other contexts can see the periodic nature of the Fourier approximation and the need for a high number of terms to accurately approximate the sharp changes in the original time series of the pixel.

Real world video (from the perspective of each individual pixel) generally follows this behavior whenever there is motion. It doesn’t matter if the motion is due to a foreground object moving, or the the entire camera moving within the scene. At the pixel level (looking at the time history of a single pixel) the values change more like a random unpredictable step function with no meaningful frequency information (beyond steady vs. changing.)

**The important take away:** When there is visual motion in video
(either due to objects moving or the camera moving) the Fourier series
approximation generally shows energy at all the different non-zero
frequency modes, and also a need for a high number of terms to
accurately approximate the original time series step behavior.

This means that DMD can show the difference between moving or non-moving regions of video, but in the general case, very little useful information can be extracted from the non-zero frequency modes beyond determining areas that have pixel values change versus areas with constant pixel values (background.)

# The stationary camera hack

With DMD the zero-frequency mode corresponds to the steady state value
of each pixel. Conceptually this is **very** similar to the average
value of the pixel over the time spanned by the video clip. It is
technically not exactly the same as the average, but it is very very
close, and close enough that the end results could be though of
intuitively as equivalent.

Consider the plot of the same arbitrarily selected pixel in the previous example (with the bike passing through it.) Here is the plot of the original pixel value versus the value of the DMD zero frequency mode approximation.

Next is a plot of the zero frequency weightings (the zero frequency mode.) As you can see the bike has been [almost] entirely removed from the scene. This is the DMD magic for scene segmentation. This is the critical observation that allows DMD to be applied to visual scene segmentation in video with a fixed camera.

The foreground (moving) portion of each frame could be reconstructed by summing the non-zero frequency modes (sum of weights * basis_function). However it is generally easier (and faster) to simply subtract the background from the current frame because the sum of all the modes is the full approximation to the original scene.