Lumina: An AI-Augmented Multiscale Material Informatics Framework for Extreme Aero-Chemo-Thermo-Mechanical Regimes
Pradeep Kumar Seshadri, Vigneshwaran N, Sudaroli Dhananjeyan, Karthikeyan S, Navbila K, Sridhar S, Subhadevi K, Hari Sree Charan H, Abdul Azeez A, Jeswin Mickle, Harsha C

TL;DR
Lumina is a modular Python framework that centralizes multiscale material data, enabling improved visualization, validation, and machine learning for extreme aerospace regimes.
Contribution
It introduces a unified, schema-independent data repository with computational and AI tools to advance material informatics in defense and aerospace applications.
Findings
Centralizes diverse multiscale material data in a unified repository.
Provides visualization tools for model fitting and experimental design optimization.
Integrates conversational AI for natural language material retrieval.
Abstract
Predictive simulations and experimental design involving extreme aero-chemo-thermo-mechanical regimes require high-fidelity material representation across diverse physical states. However, data for metals, polymers, and propellants, explosives, and pyrotechnics (PEP) remain fragmented, obstructing traceability for formulators, experimentalists, and simulation engineers. This work introduces Lumina, a modular Python-based informatics framework that centralizes multiscale material data from atomistic simulation datasets to macro-scale experimental records, within a unified repository. Lumina employs a hierarchical XML-based schema and a dynamic runtime parsing mechanism to enable schema-independent parameter extraction. Beyond storage, the platform provides computational modules to visualize model fits, allowing experimentalists to optimize design of experiments (DoE) and formulators to…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
