M:3L Lab
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Research and engineering

M:3L research

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

AI Systems

Agentic & Conversational AI Systems

M:3L develops tool-using AI systems that retrieve evidence, remember time-sensitive events, coordinate specialized tools, and support grounded conversations.

agentic AIAI agentsengineering agentsscientific agentstool-using LLMs

Spatial and Engineering Intelligence

3D Intelligence / Computer Vision

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.

3D AI3D intelligencespatial intelligencegeometric AI3D scene understanding

CAD Intelligence and Multimodal CAD Reconstruction

M:3L develops multimodal systems that reconstruct structured, editable, executable engineering representations from point clouds, multi-view images, and text.

CAD AICAD intelligenceCAD reconstructionCAD generationparametric CAD

Scientific AI

Physics-Driven Machine Learning

M:3L develops machine-learning systems constrained and guided by physical laws for computational mechanics and scientific computing.

scientific machine learningSciMLphysics-driven machine learningphysics-informed machine learningphysics-constrained AI

Differentiable Finite-Element Methods

M:3L uses differentiable finite element methods to connect mechanics simulation with gradient-based optimization and machine learning.

differentiable finite element methodsdifferentiable FEMdifferentiable simulationdifferentiable mechanicsdifferentiable physics

Computational Mechanics

Metamaterials and Wave Control

M:3L studies inverse design of elastic metamaterials, wave-cloaking structures, and programmable wave propagation.

elastic metamaterialsmechanical metamaterialstransformation elastodynamicselastic cloakingRayleigh waves

3D / CAD / Agents / Simulation / Optimization

M:3L's research areas form a connected capability map rather than unrelated silos. The convergence direction is:

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

  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, not a claim that every component is already deployed as one finished production system.