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Efficient framework for building surrogates of multidisciplinary systems using the adaptive multi-index stochastic collocation (AMISC) technique.

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Efficient framework for building surrogates of multidisciplinary systems using the adaptive multi-index stochastic collocation (AMISC) technique.

⚙️ Installation

Ensure you are using Python 3.11 or later. You can install the package from PyPI using pip:

pip install amisc

If you are using pdm in your own project, then you can use:

pdm add amisc

# Or in editable mode from a local clone...
pdm add -e ./amisc --dev

📍 Quickstart

import numpy as np

from amisc import System

def fun1(x):
    y = x * np.sin(np.pi * x)
    return y

def fun2(y):
    z = 1 / (1 + 25 * y ** 2)
    return z

system = System(fun1, fun2)

system.inputs()['x'].domain = (0, 1)   # set domain of surrogate for `x`
system.outputs()['y'].domain = (0, 1)  # set domain of surrogate for `y`

system.fit()

x_test = system.sample_inputs(10)
y_test = system.predict(x_test)

🏗️ Contributing

See the contribution guidelines.

📖 Reference

AMISC paper [1].

Made with the copier-numpy template.

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Efficient framework for building surrogates of multidisciplinary systems using the adaptive multi-index stochastic collocation (AMISC) technique.

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