{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Using Data-Driven Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to the physics-based modeling functionalities described so far, ProgPy also includes a framework for implementing data-driven models. \n",
    "\n",
    "A data-driven model is a model where the behavior is learned from data. In ProgPy, data-driven models derive from the parent class `progpy.data_models.DataModel`. A common example of a data-driven model is one that uses neural networks (e.g., `progpy.data_models.LSTMStateTransitionModel`). \n",
    "\n",
    "Some data-driven methodologies we will be exploring in this section include [Long Short-Term Memory (LSTM)](https://nasa.github.io/progpy/api_ref/progpy/DataModel.html?highlight=surrogate#lstmstatetransitionmodel), [Dynamic Mode Decomposition (DMD)](https://nasa.github.io/progpy/api_ref/progpy/DataModel.html?highlight=surrogate#dmdmodel), and [Polynomial Chaos Expansion (PCE)](https://nasa.github.io/progpy/api_ref/progpy/DataModel.html?highlight=surrogate#polynomialchaosexpansion). The data-driven architecture also includes [surrogate models](https://nasa.github.io/progpy/api_ref/progpy/DataModel.html?highlight=surrogate#from-another-prognosticsmodel-i-e-surrogate), which can be used to create models that approximate the original/higher-fidelity models, generally resulting in a less accurate model that is more computationally efficient.\n",
    "\n",
    "For more information, refer to the [`DataModel` documentation](https://nasa.github.io/progpy/api_ref/progpy/DataModel.html).\n",
    "\n",
    "Before we get started, make sure to install the data-driven dependencies using the following command:\n",
    "\n",
    "`pip install 'progpy[datadriven]'`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "\n",
    "* [General Use](#General-Use)\n",
    "    * [Building a New Model from Data](#Building-a-New-Model-from-Data)\n",
    "    * [Surrogate Models](#Surrogate-models)\n",
    "* [Long Short-Term Memory (LSTM)](#Long-Short-Term-Memory-(LSTM))\n",
    "* [Dynamic Mode Decomposition (DMD)](#Dynamic-Mode-Decomposition-(DMD))\n",
    "* [Polynomial Chaos Expansion (PCE)](#Polynomial-Chaos-Expansion-(PCE))\n",
    "* [Extending](#Extending)\n",
    "* [Conclusion](#Conclusion)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## General Use"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building a New Model from Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A version of this notebook will be added in release v1.9**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Surrogate Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A version of this notebook will be added in release v1.9**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Long Short-Term Memory (LSTM)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A version of this notebook will be added in release v1.9**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dynamic Mode Decomposition (DMD)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A version of this notebook will be added in release v1.9**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Polynomial Chaos Expansion (PCE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A version of this notebook will be added in release v1.9**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extending"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A version of this notebook will be added in release v1.9**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we were able to look at various ways to use data-driven models. The next section __[06 Combining Models](06_Combining%20Models.ipynb)__ examines how prognostics models can be combined."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.11.0 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python",
   "version": "3.12.0"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}