Metadata-Version: 2.1
Name: amisc
Version: 0.6.0
Summary: Efficient framework for building surrogates of multidisciplinary systems using the adaptive multi-index stochastic collocation (AMISC) technique.
Author-Email: Joshua Eckels <eckelsjd@umich.edu>
License: GPL-3.0
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Intended Audience :: Science/Research
Project-URL: Repository, https://github.com/eckelsjd/amisc.git
Project-URL: Documentation, https://eckelsjd.github.io/amisc/
Requires-Python: >=3.11
Requires-Dist: numpy>=2.0
Requires-Dist: scipy>=1.14
Requires-Dist: matplotlib>=3.9
Requires-Dist: networkx>=3.2
Requires-Dist: pyyaml>=6.0.2
Requires-Dist: pydantic>=2.9.1
Requires-Dist: dill>=0.3.9
Description-Content-Type: text/markdown

![Logo](https://raw.githubusercontent.com/eckelsjd/amisc/main/docs/assets/amisc_logo_text.svg)
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[![PyPI](https://img.shields.io/pypi/v/amisc?logo=python&logoColor=%23cccccc)](https://pypi.org/project/amisc)
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![Code Coverage](https://img.shields.io/badge/coverage-89%25-yellowgreen?logo=codecov)
[![Algorithm description](https://img.shields.io/badge/DOI-10.1002/nme.6958-blue)](https://doi.org/10.1002/nme.6958)

Efficient framework for building surrogates of multidisciplinary systems using the adaptive multi-index stochastic collocation ([AMISC](https://onlinelibrary.wiley.com/doi/full/10.1002/nme.6958))  technique.

## ⚙️ Installation
Ensure you are using Python 3.11 or later. You can install the package from PyPI using pip:
```shell
pip install amisc
```
If you are using [pdm](https://github.com/pdm-project/pdm) in your own project, then you can use:
```shell
pdm add amisc

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

## 📍 Quickstart
```python
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](https://github.com/eckelsjd/amisc/blob/main/CONTRIBUTING.md) guidelines.

## 📖 Reference
AMISC paper [[1](https://onlinelibrary.wiley.com/doi/full/10.1002/nme.6958)].

<sup><sub>Made with the [copier-numpy](https://github.com/eckelsjd/copier-numpy.git) template.</sub></sup>
