rombus 1.2.1

Reduced order modeling for the masses

Rombus: Helps you qucikly and easily compute slow and complex models

Rombus is a tool for building reduced order models (ROMs): matrix representations of arbitrary models which can be rapidly and easily computed for arbitrary parameter sets.

Building a ROM with Rombus is easy. All you need to do is install it like so:

$ pip install rombus

define your model like this (in this trivial case, a file named my_model.py specifying a simple second-order polynomial):

from numpy import ndarray, polyval, linspace
from rombus.model import RombusModel
from typing import NamedTuple


class Model(RombusModel):
    """Class for creating a ROM for the function y(x)=a2*x^2+a1*x+a0"""

    coordinate.set("x", 0.0, 10.0, 11, label="$x$")

    ordinate.set("y", label="$y(x)$")

    params.add("a0", -10, 10)
    params.add("a1", -10, 10)
    params.add("a2", -10, 10)

    def compute(self, p: NamedTuple, x: ndarray) -> ndarray:
        """Compute the model for a given parameter set."""
        return polyval([p.a2, p.a1, p.a0], x)

and specify a set of points (in this case, the file my_model_samples.py) to build your ROM from:

-10, -10,-10
-10,  10,-10
-10, -10, 10
-10,  10, 10
 10, -10,-10
 10,  10,-10
 10, -10, 10
 10,  10, 10

You build your ROM like this:

$ rombus build my_model:Model my_model_samples.csv

This produces an HDF5 file named my_model.hdf5. You can then use your new ROM in your Python projects like this:

from rombus.rom import ReducedOrderModel

ROM = ReducedOrderModel.from_file('my_model.hdf5')
sample = ROM.model.sample({"a0":0,"a1":0,"a2":1})
model_ROM = ROM.evaluate(sample)
for x, y in zip(ROM.model.domain,model_ROM):
    print(f"{x:5.2f} {y:6.2f}")

which generates the output:

 0.00   0.00
 1.00   1.00
 2.00   4.00
 3.00   9.00
 4.00  16.00
 5.00  25.00
 6.00  36.00
 7.00  49.00
 8.00  64.00
 9.00  81.00
10.00 100.00