Dynamic Data Driven Methods for Self-aware Aerospace Vehicles Conference Paper uri icon

abstract

  • A self-aware aerospace vehicle can dynamically adapt the way it performs missions by gathering information about itself and its surroundings and responding intelligently. Achieving this DDDAS paradigm enables a revolutionary new generation of self-aware aerospace vehicles that can perform missions that are impossible using current design, flight, and mission planning paradigms. To make self-aware aerospace vehicles a reality, fundamentally new algorithms are needed that drive decision-making through dynamic response to uncertain data, while incorporating information from multiple modeling sources and multiple sensor fidelities. In this work, the specific challenge of a vehicle that can dynamically and autonomously sense, plan, and act is considered. The challenge is to achieve each of these tasks in real time-executing online models and exploiting dynamic data streams-while also accounting for uncertainty. We employ a multifidelity approach to inference, prediction and planning-an approach that incorporates information from multiple modeling sources, multiple sensor data sources, and multiple fidelities. 2012 Published by Elsevier Ltd.

published proceedings

  • PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE, ICCS 2012

author list (cited authors)

  • Allaire, D., Biros, G., Chambers, J., Ghattas, O., Kordonowy, D., & Willcox, K.

complete list of authors

  • Allaire, D||Biros, G||Chambers, J||Ghattas, O||Kordonowy, D||Willcox, K

publication date

  • January 1, 2012 11:11 AM