# M:3L Research

> M:3L combines machine learning with geometry, temporal structure, and physical law.

M:3L is a hybrid research and engineering laboratory working across machine learning, large-scale AI systems, computational physics, geometry, and mechanics.

## AI Systems

- [Agentic AI](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.
- [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.

## Spatial and Engineering Intelligence

- [3D Intelligence](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](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.

## Scientific AI

- [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.

## Computational Mechanics

- [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.

## Cross-disciplinary convergence

**Perception → 3D Understanding → Geometry → CAD → Simulation → Optimization → Autonomous Engineering**

M:3L's research directions are complementary rather than isolated. A possible research pipeline is:

1. Perceive a physical environment from images or point clouds
2. Understand objects, language references, layouts, and spatial structure
3. Recover geometric representations
4. Convert or reconstruct geometry into editable CAD
5. Generate or modify CAD parametrically
6. Create simulation-ready models
7. Run numerical or finite element simulation
8. Evaluate engineering objectives and constraints
9. Optimize the design
10. Allow a tool-using AI agent to orchestrate and validate the loop

This is a convergence of M:3L research capabilities. It is not a claim that every component is already deployed as one finished production system.

## Related resources

- [Publications](https://m3l.am/publications.md): Peer-reviewed papers and conference work.
- [News and projects](https://m3l.am/news.md): Grants, collaborations, project updates, and events.
- [Team](https://m3l.am/team.md): Researchers and engineers at M:3L.
- [Detailed machine-readable guide](https://m3l.am/llms-full.txt): Projects, benchmarks, methods, technologies, and collaboration areas.