Physics-informed Machine Learning Models for Go/No-go Criteria on Reactive Metamaterials

Seungjoon Lee, Kibaek Lee, Alberto Hernández, D. Scott Stewart

Research output: Contribution to journalConference articlepeer-review


We present a physics-informed machine learning framework for predicting Go/No-Go criteria for reactive metamaterials and study shock propagation through a one-dimensional laminate structure. The laminate material was composed of an HMX bed with equally distributed 2mm thick copper pillars. The Wide-Ranging equation of state (WR EOS) was used to model HMX while the Romenski EOS was used for the elastic regime of copper, with the assumption of perfect plasticity. The shock was initiated by using an aluminum impactor and gauges were placed at the entry of the first copper pillar and exit of the last pillar. A modified machine learning model was then developed to predict the Go/No-Go criteria for the laminate structure. The proposed model only uses short-time measurements for predicting this behavior, that leads to large reductions in computational cost at higher dimensions. This framework suggests a data-driven guideline for the design of optimal laminate structures (e.g. number of copper pillars, thickness, and distribution).

Original languageEnglish (US)
Article number280002
JournalAIP Conference Proceedings
Issue number1
StatePublished - Sep 26 2023
Externally publishedYes
Event22nd Biennial American Physical Society Conference on Shock Compression of Condensed Matter, SCCM 2022 - Anaheim, United States
Duration: Jul 10 2022Jul 15 2022

ASJC Scopus subject areas

  • General Physics and Astronomy


Dive into the research topics of 'Physics-informed Machine Learning Models for Go/No-go Criteria on Reactive Metamaterials'. Together they form a unique fingerprint.

Cite this