Theory-Guided Machine Learning for Process Simulation of Advanced Composites

03/30/2021
by   Navid Zobeiry, et al.
0

Science-based simulation tools such as Finite Element (FE) models are routinely used in scientific and engineering applications. While their success is strongly dependent on our understanding of underlying governing physical laws, they suffer inherent limitations including trade-off between fidelity/accuracy and speed. The recent rise of Machine Learning (ML) proposes a theory-agnostic paradigm. In complex multi-physics problems, however, creating large enough datasets for successful training of ML models has proven to be challenging. One promising strategy to bridge the divide between these approaches and take advantage of their respective strengths is Theory-Guided Machine Learning (TGML) which aims to integrate physical laws into ML algorithms. In this paper, three case studies on thermal management during processing of advanced composites are presented and studied using FE, ML and TGML. A structured approach to incrementally adding increasingly complex physics to training of TGML model is presented. The benefits of TGML over ML models are seen in more accurate predictions, particularly outside the training region, and ability to train with small datasets. One benefit of TGML over FE is significant speed improvement to potentially develop real-time feedback systems. A recent successful implementation of a TGML model to assess producibility of aerospace composite parts is presented.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 5

page 14

06/05/2018

Reduced-Order Modeling through Machine Learning Approaches for Brittle Fracture Applications

In this paper, five different approaches for reduced-order modeling of b...
10/09/2021

A Review of Physics-based Machine Learning in Civil Engineering

The recent development of machine learning (ML) and Deep Learning (DL) i...
02/02/2020

Using Machine Learning for Model Physics: an Overview

In the overview, a generic mathematical object (mapping) is introduced, ...
01/07/2022

Automated Dissipation Control for Turbulence Simulation with Shell Models

The application of machine learning (ML) techniques, especially neural n...
01/15/2018

Improving Orbit Prediction Accuracy through Supervised Machine Learning

Due to the lack of information such as the space environment condition a...
07/30/2020

Machine learning for complete intersection Calabi-Yau manifolds: a methodological study

We revisit the question of predicting both Hodge numbers h^1,1 and h^2,1...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.