# A machine learning framework for computationally expensive transient models

## TL;DR

This paper introduces a machine learning framework that combines traditional simulation tools with forecasting methods to reduce computational costs in transient dynamic system modeling, maintaining accuracy.

## Contribution

It presents a novel ensemble approach integrating DEM, ARIMA, and machine learning to efficiently simulate large-scale transient systems.

## Key findings

- The framework achieves significant computational savings.
- The machine learning model maintains high accuracy.
- Good agreement with existing literature results.

## Abstract

The promise of machine learning has been explored in a variety of scientific disciplines in the last few years, however, its application on first-principles based computationally expensive tools is still in nascent stage. Even with the advances in computational resources and power, transient simulations of large-scale dynamic systems using a variety of the first-principles based computational tools are still limited. In this work, we propose an ensemble approach where we combine one such computationally expensive tool, called discrete element method (DEM), with a time-series forecasting method called auto-regressive integrated moving average (ARIMA) and machine-learning methods to significantly reduce the computational burden while retaining model accuracy and performance. The developed machine-learning model shows good predictability and agreement with the literature, demonstrating its tremendous potential in scientific computing.

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Source: https://tomesphere.com/paper/1907.05928