RFDT Solver and WiTwin Simulator are undergoing internal testing as we prepare for public release. The functions are limited. Sign up to receive updates.

The RF Digital Twin

Physical · Differentiable · Rendering
Next-Gen Wireless Sensing and Communication Research Platform

This is an internal preview version. Full release coming before MobiCom'26.
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RFDT has been conditionally accepted by MobiCom'26

Currently in Internal Testing

RFDT is undergoing internal testing as we prepare for public release. We're working hard to ensure a stable and polished experience for everyone.

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Build the Future of Wireless Simulation

A complete platform for RF simulation, optimization, and research

Multi-method electromagnetic solver

Next Generation EM Simulation

Choose the right simulation method for your needs. From fast ray tracing for large-scale scenarios to full-wave solvers for high-accuracy analysis.

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TXRX
Paths: 847Bounces: 3Time: 2.3ms
Gradient computation for optimization

End-to-End Differentiable

Compute gradients through the entire simulation pipeline. Enable neural network integration and inverse design optimization.

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LossObservationSimulated SignalSignal GradientRFDTSimulatorParameterizationSceneMeshingGradientParametersValueGradForwardGradient
Deep learning powered digital twin

Integrating with Neural Networks

Seamlessly integrate physics-based simulation with deep neural networks. Train end-to-end models that combine learned representations with accurate physical modeling.

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LossPhysical Twin IntrinsicRFSignalInputDNNShapeMaterialFormWaveSensorMotionRFDTSimulatorRFSignalOutputFeed ForwardPhysics Model
Node-based configuration interface

Visual Editor

Design and configure simulation scenarios with an intuitive visual editor. Real-time preview and parameter adjustment.

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HIERARCHY
▼ Scene
├ Transmitter
├ Building_01
├ Building_02
└ Receiver
3D Viewport
NODE EDITOR
∂ HyperparamTX PosFreqSceneComputeChannelPaths
PROPERTIES
Position0.0, 1.5, 0.0
Frequency77 GHz
Flexible scripting interface

Python SDK

Full programmatic control with a clean Python API. Integrate with your existing ML workflows and data pipelines.

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simulation.py
import rfdt
# Create scene
scene = rfdt.Scene()
scene.add_transmitter(pos=[0,0,0])
# Run simulation
result = scene.simulate()
grad = result.backward()
AI Assistant

Add a human and dog running in a living room. Use 60GHz MIMO radar sensing.

Creating scene with animated targets...

scene.load("living_room")
scene.add_actor("human")
scene.add_radar(freq=60e9)
Ready
Ask AI Agent to modify simulation...

Developers

Developer Name

Lead Developer

Research focus and brief description of contributions to the project.

Developer Name

Core Developer

Research focus and brief description of contributions to the project.

Citation

If you use RF Digital Twin in your research, please cite our paper

BibTeX
@inproceedings{rfdigitaltwin2026,
  title={Physically Accurate Differentiable Inverse Rendering for Radio Frequency Digital Twin},
  author={},
  booktitle={Proceedings of the 32nd Annual International Conference on Mobile Computing and Networking (MobiCom)},
  year={2026}
}
Waiting for publication finalization

Licensing

Research

Free

For academic and non-commercial research purposes

  • Full access to simulation capabilities
  • Academic publications allowed
  • Community support
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Commercial

Contact Us

For commercial and enterprise applications

  • Production deployment rights
  • Priority support
  • Custom integration assistance
Contact Us