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"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"# Introduction to ProgPy" | ||
"# Introduction to ProgPy (2024 NASA Software of the Year!)" | ||
] | ||
}, | ||
{ | ||
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"* Advanced Noise Representation: e.g., Other distributions\n", | ||
"* Complex Future Loading Methods: E.g., moving average, loading with uncertainty or functions of state\n", | ||
"* Custom Events: e.g., warning thresholds\n", | ||
"* Data-driven models\n", | ||
"* Derived Parameters: parameters that are functions of other parameters\n", | ||
"* Direct Models: Models of state, future_loading -> Time of Event without state transition\n", | ||
"* Linear Models\n", | ||
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"metadata": {}, | ||
"source": [ | ||
"* Combination Models: https://nasa.github.io/progpy/prog_models_guide.html#combination-models and 06_Combining_Models\n", | ||
"* Dynamic Step Size" | ||
"* Dynamic Step Size\n", | ||
"* Integration Methods\n", | ||
"* Serialization\n", | ||
"* prog_server" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"## Combination Models" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"https://nasa.github.io/progpy/prog_models_guide.html#combination-models\n", | ||
"\n", | ||
"This section demonstrates how prognostic models can be combined. There are two times in which this is useful: \n", | ||
"## Closing\n", | ||
"\n", | ||
"1. When combining multiple models of different inter-related systems into one system-of-system model (i.e., [Composite Models](https://nasa.github.io/progpy/api_ref/prog_models/CompositeModel.html)), or\n", | ||
"2. Combining multiple models of the same system to be simulated together and aggregated (i.e., [Ensemble Models](https://nasa.github.io/progpy/api_ref/prog_models/EnsembleModel.html) or [Mixture of Expert Models](https://nasa.github.io/progpy/api_ref/progpy/MixtureOfExperts.html)). This is generally done to improve the accuracy of prediction when you have multiple models that each represent part of the behavior or represent a distribution of different behaviors.\n", | ||
"\n", | ||
"For this example we will combine the competing models using a Mixture of Expert Model" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"## Closing" | ||
"Thank you for attending this tutorial. ProgPy is a collaborative effort, including NASA and external collaborators. If you're interested in contributing or learning more, reach out at [email protected]" | ||
] | ||
}, | ||
{ | ||
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