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Operator inference for data-driven, non-intrusive model reduction of dynamical systems.

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Operator Inference in Python

This is a Python implementation of Operator Inference for learning projection-based polynomial reduced-order models of dynamical systems. The procedure is data-driven and non-intrusive, making it a viable candidate for model reduction of "glass-box" systems. The methodology was introduced in [1].

See the Wiki for mathematical details and API documentation. See this repository for a MATLAB implementation.

Quick Start


Install the package from the command line with the following single command (requires pip).

$ python3 -m pip install --user rom-operator-inference

See the wiki for other installation options.


Given a basis matrix Vr, snapshot data X, and snapshot time derivatives Xdot, the following code learns a reduced model for a problem of the form dx / dt = c + Ax(t), then solves the reduced system for 0 ≤ t ≤ 1.

import numpy as np
import rom_operator_inference as roi

# Define a model of the form  dx / dt = c + Ax(t).
>>> model = roi.InferredContinuousROM(modelform="cA")

# Fit the model to snapshot data X, the time derivatives Xdot,
# and the linear basis Vr by solving for the operators c_ and A_.
>>>, X, Xdot)

# Simulate the learned model over the time domain [0,1] with 100 timesteps.
>>> t = np.linspace(0, 1, 100)
>>> x_ROM = model.predict(X[:,0], t)


The examples/ folder contains scripts and notebooks that set up and run several examples:

Contributors: Renee Swischuk, Shane McQuarrie, Elizabeth Qian, Boris Kramer, Karen Willcox.


These publications introduce, build on, or use Operator Inference. Entries are listed chronologically.

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