paramrf 0.19.0

Parametric radio frequency modeling

ParamRF, or pmrf, is an open-source radio frequency (RF) modeling framework. It provides a declarative syntax for creating RF circuit and surrogate models using JAX.

The library provides tools for model simulation, optimization, fitting, statistical analysis, and Bayesian inference.

Version:

Author:

Gary Allen

Homepage:

https://github.com/gvcallen/paramrf

Docs:

https://gvcallen.github.io/paramrf

Paper:

https://doi.org/10.48550/arXiv.2510.15881

Features

• Declarative syntax: Models can be composed and defined using a self-documenting, object-oriented syntax.

• Hierarchical modeling: By nesting models within models, it is easy to create deep, hierarchical circuits.

• Differentiable: Since the library is built on jax (as opposed to numpy), derivatives are available using auto-differentiation, enabling faster performance and new design opportunities.

• Hardware flexible: Functions are compiled just-in-time (JIT), reducing overhead and allowing computation on high-performance hardware (CPU, GPU, TPU).

• Extensible: Power users can easily add additional models and algorithms by extending the library’s built-in classes and interfaces.

Installation

ParamRF can be installed directly using pip (requires Python 3.11+):

$ pip install paramrf

Example

The example below shows how to define and optimize an RLC model to satisfy a given goal function. See the documentation for more examples.

import pmrf as prf
from pmrf.models import Resistor, Inductor, Capacitor

model = Resistor(50) ** Inductor(prf.Value(1.0, scale=1e-9)) ** Capacitor(prf.Value(1.0, scale=1e-12))
goal = prf.evaluators.Goal('s11_db', '<', -20)

opt_freq = prf.Frequency(3, 4, 101, 'GHz')
result = prf.optimize.minimize(goal, model, opt_freq, solver=prf.optimize.ScipyMinimize())

plot_freq = prf.Frequency(1, 6, 101, 'GHz')
result.model.plot_s_db(plot_freq, m=0, n=0)

Next steps

• For an overview of the library’s features, see the examples page.

• For step-by-step guides that you can follow, check out the tutorials page.

• To delve a bit deeper into understanding the library’s core building blocks and philosophy, see the core concepts page.

Optional dependencies

Several additional dependencies are required/recommended for more advanced use-cases.

For Bayesian inference, you may need this fork of distreqx:

$ pip install git+https://github.com/gvcallen/distreqx

For BlackJAX’s Bayesian solvers:

$ pip install git+https://github.com/handley-lab/blackjax.git@v0.1.0-beta

For the PolyChord solver:

$ pip install git+https://github.com/PolyChord/PolyChordLite.git anesthetic mpi4py

Citation

If you have used ParamRF for academic work, please cite the original paper (https://doi.org/10.48550/arXiv.2510.15881): as:

G.V.C. Allen, D.I.L. de Villiers, (2025). ParamRF: A JAX-native Framework for Declarative Circuit Modelling. arXiv, https://doi.org/10.48550/arXiv.2510.15881.

or using the BibTeX:

@article{paramrf,
   doi = {10.48550/arXiv.2510.15881},
   url = {https://doi.org/10.48550/arXiv.2510.15881},
   year = {2025},
   month = {Oct},
   title = {ParamRF: A JAX-native Framework for Declarative Circuit Modelling},
   author = {Gary V. C. Allen and Dirk I. L. de Villiers},
   eprint = {2510.15881},
   archivePrefix = {arXiv},
   primaryClass = {cs.OH},
}