rfx

Differentiable FDTD for RF/Microwave — powered by JAX

rfx is a 3-D finite-difference time-domain (FDTD) electromagnetic simulator built in JAX. It targets RF and microwave engineers who want practical field simulation, RF workflow tooling, and native gradient support for inverse design in Python.

Current public status

Use rfx with two simple lanes in mind:

Lane	Use it for	Status
Recommended default: uniform Cartesian Yee RF workflows	cavity and waveguide studies, patch-style resonance workflows, probes, Harminv, selected port/S-parameter workflows, and benchmarked far-field workflows	best place to start
Experimental / under active validation	non-uniform meshes, distributed execution, Floquet/Bloch workflows, SBP-SAT subgridding, coaxial and advanced port workflows, and inverse-design extensions	useful for experiments; check current limitations

The docs keep advanced features visible, but they should not be read as blanket guarantees.

Why rfx

Feature	Detail
GPU acceleration	JAX/JIT execution for large 3-D grids
Differentiable simulation	`jax.grad` through time-domain workflows for inverse design
RF workflow tools	materials, sources, probes, ports, S-parameter helpers, Harminv, far-field utilities
Recommended examples	current runnable scripts under `examples/crossval/` and `examples/inverse_design/`
Experimental lanes	non-uniform mesh, distributed runs, Floquet/Bloch, SBP-SAT subgridding, coaxial/advanced ports

Quick install

pip install rfx-fdtd

GPU support depends on your JAX/CUDA environment:

pip install "jax[cuda12]" rfx-fdtd

Ten-line example

from rfx import Simulation, Box, GaussianPulse

sim = Simulation(freq_max=4e9, domain=(0.08, 0.06, 0.025), boundary="cpml")
sim.add(Box((0.0, 0.0, 0.0), (0.08, 0.06, 0.0016)), material="fr4")
sim.add(Box((0.02, 0.01, 0.0016), (0.049, 0.049, 0.0016)), material="pec")
sim.add(Box((0.0, 0.0, 0.0), (0.08, 0.06, 0.0)), material="pec")
sim.add_source((0.029, 0.03, 0.0008), "ez",
               waveform=GaussianPulse(f0=2.4e9, bandwidth=0.8))
sim.add_probe((0.029, 0.03, 0.0008), "ez")

result = sim.run(n_steps=8000)
modes = result.find_resonances(freq_range=(1.5e9, 3.5e9))
print(f"Resonance: {modes[0].freq/1e9:.4f} GHz")

Documentation by topic

Getting Started

Installation — Python/JAX install, GPU notes, dev setup
Quick Start — first simulation with the current high-level API
Your First Patch Antenna — current-source resonance workflow

Modeling & Setup

Simulation API — Simulation, Result, auto_configure, ports, NTFF, and design helpers
Materials & Geometry — library materials, Debye/Lorentz, CSG shapes, PCB stackup
Sources & Ports — point sources, lumped/wire ports, waveguide ports, experimental Floquet/coaxial surfaces
Probes & S-Parameters — DFT probes, S-matrix helpers, Harminv, de-embedding, exports
Non-Uniform Mesh — experimental thin-substrate workflows
Waveguide Ports — rectangular waveguide modal workflows
Floquet Ports — experimental Bloch-periodic unit-cell workflows

Analysis & Validation

Cross-Validation & Accuracy — short public validation overview
Convergence Study — mesh refinement workflow
Far-Field & RCS — NTFF radiation patterns and scattering workflows
Antenna Metrics — gain, efficiency, beamwidth, bandwidth, F/B ratio
Visualization & Analysis — plotting, exports, post-processing
Solver Comparison — workflow comparison vs. Meep and OpenEMS

Design & Optimization

Inverse Design — autodiff-driven topology, shape, and far-field optimization
Topology Optimization — density-based inverse design
Parametric Sweeps — sequential sweep and jax.vmap batch evaluation
Material Fitting — Debye/Lorentz fitting and differentiable refinement
Patch Antenna Design — rectangular patch workflow
Microstrip Filter Design — experimental microstrip workflow

Advanced / Experimental

Advanced Features — distributed execution, mixed precision, nonlinear materials, and research-style workflows
Conformal PEC — Dey-Mittra method for curved conductors
SBP-SAT Subgridding — experimental local mesh refinement
Gradient Behavior — where gradients are strong vs noisy
Geometry & Limitations — supported workflows and current trade-offs

Secondary hubs

Examples — recommended public runnable paths
Validation — public support and validation overview
API — curated public API contract
Generated API — subordinate generated symbol reference