paramrf 0.17.2

Parametric radio frequency modeling, optimization and sampling

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

The library also provides tools for model 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: Allows for the definition of models using either a self-documenting, declarative syntax, or via compositional techniques such as cascading or nodal circuit building. Also, since models can contain other models, this provides a natural means of building deeply nested, hierarchical models.

• Differentiable: Since the framework is built on top of jax (as opposed to numpy), all models can be differentiated with respect to both frequency and parameters using auto-differentiation. This allows for both more efficient and flexible optimization, as well as unique design and analysis opportunities.

• High performance and hardware flexible: Since models are compiled using jax with Just-In-Time (JIT) compilation, model performance is improved, and models can also be computed on high-performance hardware (CPU, GPU, TPU).

• Built-in optimization and inference wrappers: Provides built-in wrappers for frequentist optimization and Bayesian inference in pmrf.optimize and pmrf.infer, as well as high-level wrappers for fitting models to data in pmrf.fitting.

• Extensibility: Designed to be extendable, such that additional models, fitting algorithms, cost functions, sampling routines etc. can easily be implemented.

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 a simple RLC model to satisfy a given goal function. See the documentation for more complex examples, or have a look at the tutorials folder on GitHub.

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

freq = prf.Frequency(1, 10, 101, 'GHz')
rlc_model = Resistor(50) ** Inductor(Scaled(1.0, 1e-9)) ** Capacitor(Scaled(1.0, 1e-12))

opt_freq = prf.Frequency(4, 6, 101, 'GHz')
goal = prf.evaluators.Goal('s11_db', '<', -20)

result = prf.optimize.minimize(goal, rlc_model, opt_freq, solver=prf.optimize.ScipyMinimize())
result.model.plot_s_db(freq, m=0, n=0)

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},
}