MetoSim Documentation

MetoSim is a cloud-native simulation platform for nanophotonics and meta-optics device design. Researchers interact through a Python SDK while GPU-accelerated FDTD computation runs remotely on cloud infrastructure.

MetoSim is the first pillar of the Meto Platform — a full-stack development environment for meta-optics technologies.

Platform	Purpose	Status
MetoSim	Simulation & computational design	V1 MVP
MetoFab	Nanofabrication workflows	Planned
MetoLab	Experimental validation	Planned

What MetoSim does

• FDTD electromagnetic simulation on cloud GPUs (NVIDIA B200 / A100)
• Python-first SDK — pip install metosim, submit from Jupyter
• Pydantic-validated configs — catch errors before burning GPU time
• HDF5 results with SHA-256 checksums for reproducibility
• Built-in materials — Si, SiO₂, TiO₂, Au, Al, Si₃N₄ at telecom wavelengths
• Visualization — plot_field() renders publication-ready figures

Who it's for

Research Scientists — Run EM simulations without managing infrastructure. No COMSOL license, no local GPU, no IT tickets.

Photonic Engineers — Iterate on metasurface geometry with structured configs that feed directly into MetoFab.

ML Researchers (V3) — Generate large simulation datasets for training surrogate models.

How it works

Your Python script
       ↓
  MetoSim SDK (validates config locally)
       ↓
  Cloud API (queues job, dispatches to GPU)
       ↓
  FDTD Engine (solves Maxwell's equations on B200)
       ↓
  HDF5 results (downloaded + checksum-verified)
       ↓
  plot_field() (visualize locally)

Quick example

import metosim

client = metosim.MetoSimClient(api_key="mts_your_key")

sim = metosim.Simulation(
    solver="fdtd",
    wavelength=1.55e-6,
    geometry=[
        metosim.Box(center=(0,0,0), size=(2e-6, 2e-6, 0.22e-6), material="Si"),
    ],
    domain_size=(4e-6, 4e-6, 4e-6),
    resolution=20e-9,
)

job = client.run(sim)
job.wait()
results = job.results()
metosim.plot_field(results, component="Ez")

Roadmap

Version	Target	Features
V1 (Now)	MVP	Single-job FDTD, Python SDK, HDF5 results
V2 (Q2 2026)	Batch	Parameter sweeps, RCWA solver, concurrent jobs
V3 (Q3 2026)	ML	Large-scale dataset generation, batch export
V4 (Q4 2026)	Inverse	Adjoint solver, AI-guided structure discovery

Next steps

• Quick Start → — Install the SDK and run your first simulation in 5 minutes
• Architecture → — Understand the system design
• API Reference → — Full endpoint documentation