# M:3L — Mathematical Modeling & Machine Learning Laboratory

> M:3L (Mathematical Modeling & Machine Learning Laboratory) is a research and engineering laboratory in Yerevan, Armenia, working across agentic AI and multi-agent systems, 3D intelligence and computer vision, CAD intelligence and multimodal CAD reconstruction, physics-driven machine learning, differentiable finite-element methods, metamaterials and wave control, and event-sequence and temporal models.

M:3L was established in June 2025 at the Institute of Mechanics of the National Academy of Sciences of the Republic of Armenia. The laboratory develops AI systems that combine data-driven learning with geometry, temporal structure, and physical laws.

## About

- [About M:3L](https://m3l.am/about.md): Laboratory identity, mission, affiliation, location, and capabilities.
- [Team](https://m3l.am/team.md): M:3L researchers, engineers, affiliations, and collaborators.
- [Contact and collaboration](https://m3l.am/contact.md): Contact details and collaboration areas.

## Research

- [Agentic AI and Multi-Agent Systems](https://m3l.am/research/agentic-ai.md): M:3L develops autonomous, tool-using single-agent and multi-agent AI systems for scientific, engineering, and enterprise workflows.
- [3D Intelligence / Computer Vision](https://m3l.am/research/3d-intelligence.md): M:3L develops geometric and spatial AI for zero-shot 3D grounding, open-vocabulary detection, scene layouts, and real-world understanding from point clouds or multi-view imagery.
- [CAD Intelligence and Multimodal CAD Reconstruction](https://m3l.am/research/cad-intelligence.md): M:3L develops multimodal systems that reconstruct structured, editable, executable engineering representations from point clouds, multi-view images, and text.
- [Physics-Driven Machine Learning](https://m3l.am/research/physics-driven-ml.md): M:3L develops machine-learning systems constrained and guided by physical laws for computational mechanics and scientific computing.
- [Differentiable Finite-Element Methods](https://m3l.am/research/differentiable-fem.md): M:3L uses differentiable finite element methods to connect mechanics simulation with gradient-based optimization and machine learning.
- [Metamaterials and Wave Control](https://m3l.am/research/metamaterials.md): M:3L studies inverse design of elastic metamaterials, wave-cloaking structures, and programmable wave propagation.
- [Event-Sequence and Temporal Models](https://m3l.am/research/event-sequence-models.md): M:3L develops value-grounded event representations that encode what happened, when it happened, and which outcome followed.

## Outputs

- [Publications](https://m3l.am/publications.md): Papers, authors, venues, summaries, arXiv records, DOI links, and code repositories.
- [News & Events](https://m3l.am/news.md): Laboratory projects, collaborations, grants, publication announcements, and events.
- [Comprehensive machine-readable guide](https://m3l.am/llms-full.txt): Detailed projects, benchmarks, methods, technologies, applications, and collaboration areas.
- [Machine-readable site index](https://m3l.am/api/site-index.json): JSON map of authoritative M:3L resources.