Book chapter
Physics-aware Deep Learning Methods for Modeling Spatiotemporal Dynamics in Energetic Materials
Energetic Materials and Techniques: Advances in Chemical Propulsion and Power Generation: Volume 1, pp.331-364
Wiley
2026
DOI: 10.1002/9783527853267.ch11
Abstract
Deep neural networks are increasingly adopted by the energetic materials community for the data-driven modeling of the initiation thermomechanics of these materials. This chapter aims to provide a gentle introduction to some of the modern deep learning methods from the angle of computational physics modeling and to review some of the well-known applications of deep learning for modeling hotspot formation and growth in energetic materials.
Details
- Title: Subtitle
- Physics-aware Deep Learning Methods for Modeling Spatiotemporal Dynamics in Energetic Materials
- Creators
- Stephen S. Baek - University of VirginiaJoseph B. Choi - University of VirginiaXinlun Cheng - University of VirginiaH. S. Udaykumar - University of Iowa
- Resource Type
- Book chapter
- Publication Details
- Energetic Materials and Techniques: Advances in Chemical Propulsion and Power Generation: Volume 1, pp.331-364
- DOI
- 10.1002/9783527853267.ch11
- Publisher
- Wiley
- Language
- English
- Date published
- 2026
- Academic Unit
- Engineering Administration; Injury Prevention Research Center; Chemical and Biochemical Engineering; Mechanical Engineering
- Record Identifier
- 9985164721202771
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