A Physics-based Machine Learning Approach for Predicting Failure in Airframe Structural Components

Abstract

This proposal addresses the development of a hybrid computational paradigm, based on synergistic coupling the adaptability of machine learning (ML) techniques based on field data with the precision and physics based approach of computational mechanics, for predicting material response to cyclic loading conditions.

Document Details

Document Type
DoD Grant Award
Publication Date
Mar 06, 2024
Source ID
FA95502310513

Entities

People

  • Samit Roy

Organizations

  • Air Force Office of Scientific Research
  • United States Air Force
  • University of Alabama

Tags

Fields of Study

  • Physics

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Computational Fluid Dynamics (CFD)
  • Structural Health Monitoring of Composite Structures.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks