radia 4.95.20

Radia 3D Magnetostatics with NGSolve Integration and TaskManager Parallelization

Radia - AI-Native Electromagnetic CAE

AI designs. Radia provides the engineering platform.

Radia is a programmable electromagnetic CAE platform built on NGSolve. It connects Python, MCP, Simulink, CAD, meshing, electromagnetic analysis, optimization, result-bearing notebooks, and visualization into one engineering workflow.

Radia is not another all-in-one solver and it is not a replacement for NGSolve. NGSolve remains the numerical foundation. Radia contributes the electromagnetic methods, open-boundary models, application workflows, and AI-facing interfaces that are needed to turn a numerical backend into a practical design platform.

Use Radia through its MCP servers. This README is the entry point, not a duplicate operating manual. Ask the relevant radia-mcp server for the current workflow, supported contract, machine-specific prerequisites, and validation steps before running a domain operation. The MCP response is designed to be executable by an agent and routes MATLAB execution through MathWorks' official MATLAB MCP Server. Start with the radia-mcp tool catalog.

Highlights

• Couple motion, control, circuits, and electromagnetic force in Simulink. Simulink owns the time-domain motion and controller model, while Radia supplies electromagnetic force/field models and LTspice supplies the drive circuit. Vector signals and interval state handoff close the loop between position, velocity, current, voltage, and force.
• Use existing LTspice circuits as the electrical subsystem. Run real device models as a sampled-data circuit plant without Simscape Electrical, with hidden execution and capacitor-voltage/inductor-current handoff between intervals. The hysteretic plant block iterates LTspice current and Radia magnetic flux within each interval, returning hysteretic back EMF to the power-electronic circuit before committing either state.
• Optimize the coupled machine, circuit, and controller together. Run parallel Simulink/LTspice trials with MATLAB-native Optuna-style studies, multiple objectives, and live Pareto monitoring.
• Turn a SPICE netlist into an editable LTspice schematic. Convert .cir or .net files to automatically laid-out .asc schematics from Python or MATLAB, then verify connectivity by converting back with LTspice.
• Use the same engineering APIs from Python and MATLAB. Radia keeps its C++ numerical kernels and Python circuit parser as single sources of truth while exposing checked MATLAB and Simulink adapters.
• Use NGSolve from MATLAB through a native MEX bridge. Persistent checked handles expose selected NGSolve meshes, finite-element spaces, coefficient and grid functions, forms, vectors, sparse matrices, and HCurl workflows to MATLAB without launching Python for each operation. The bridge preserves NGSolve's ownership of finite-element mathematics and is cross-checked against the Python path.

             position / velocity
        +------------------------------+
        |                              v
Simulink motion <--- force --- Radia / NGSolve electromagnetic model
        ^                              ^
        |                              | current
        +--- controller ---> LTspice drive circuit
                    gate / voltage command

% Netlist -> editable LTspice schematic, with round-trip connectivity check
radia.ltspice.netlistToSchematic("converter.cir", ...
    OutputFile="converter.asc", ValidateRoundTrip=true);

% Use the LTspice drive circuit inside a Simulink motion/control model
radia.simulink.buildLTspiceBlock("current_controller", ...
    Netlist="converter.cir", ...
    InputNames=["gate"; "reference"], ...
    OutputTraces=["I(L1)"; "V(out)"], ...
    SampleTime_s=1e-4);

Register the packaged blocks once after adding Radia's matlab directory to the MATLAB path:

radia.simulink.buildLibrary
sl_refresh_customizations

The Simulink Library Browser then shows one Radia library containing the Electromagnet, PCB PEEC, Motor, Stream Function, Induction Heating, LTspice, and Optuna blocks. For the machine-readable setup and compatibility contract, ask the mcp-server-radia-matlab tool matlab_simulink_library_contract.

The central idea

NGSolve owns the numerical foundation. Radia adds the missing electromagnetic engineering methods. AI and humans use the same programmable workflow.

The platform is designed for a complete engineering loop:

Natural-language intent
        |
        v
Python / MCP workflow
        |
        +--> CAD and mesh generation
        |       build123d, Cubit, Netgen, Gmsh
        |
        +--> Electromechanical co-simulation
        |       Simulink motion/control + LTspice circuits
        |       + Radia/NGSolve electromagnetic force
        |
        +--> Electromagnetic design and analysis
        |       Radia methods + NGSolve / ngsolve.bem
        |
        +--> Optimization, validation, and visualization
                Python, Jupyter, Gmsh, Netgen, result artifacts

An LLM can write and execute the Python workflow through MCP. A human operates the application through Simulink and inspects durable results in docs notebooks or native visualization tools. IH temporarily keeps a comparison workbench. The interfaces differ; the engineering model and validation artifacts are shared.

Architecture

Radia is organized as three layers.

Layer	Responsibility
Application	Magnet design, Hodograph, VIM, Eddy, Stream Function, induction heating, MagLev, motors, and other concrete workflows
Platform	Python APIs, MCP servers, Simulink application blocks, docs notebooks, build123d, Cubit, Gmsh, Netgen, validation, and result artifacts
Numerical	NGSolve finite elements and ngsolve.bem for spaces, transformations, quadrature, weak forms, BEM operators, and linear algebra

The boundary between these layers matters. Radia should extend NGSolve at the physics and application layers, while continuing to use NGSolve's public abstractions for finite-element plumbing.

What Radia owns

Radia focuses on electromagnetic capabilities that are not provided by a general-purpose finite-element backend alone:

• analytical magnetic and source fields for open regions;
• magnetic-material methods based on volume integral and charge-Gram ideas;
• surface impedance, PEEC, and low-frequency eddy-current workflows;
• topology-aware coil and conductor design;
• model reduction for repeated electromagnetic solves;
• orchestration that lets AI agents use CAD, meshing, analysis, and validation as one workflow.

What Radia delegates

Radia deliberately integrates strong existing tools instead of rebuilding them:

• NGSolve: finite-element spaces, Piola maps, curved geometry, orientations, quadrature, weak-form assembly, GridFunctions, and the numerical solve;
• ngsolve.bem: boundary-element formulations and surface operators;
• Netgen: mesh generation and notebook visualization;
• Coreform Cubit: CAD-driven mesh generation and the Cubit export interface;
• build123d: programmable CAD construction;
• Gmsh: durable mesh and field-result visualization;
• NumPy, SciPy, MKL, and proven linear-algebra libraries: numerical building blocks.

This is not only a packaging preference. It keeps Radia's development effort focused on electromagnetic methods rather than duplicating mature numerical infrastructure.

Core technologies

Electromechanical co-simulation with LTspice and Simulink

Radia uses Simulink as the dynamic-system orchestrator: mechanical position and velocity update the electromagnetic model, electromagnetic force updates the motion model, and the LTspice drive circuit exchanges current, voltage, and control signals with both. This motion-circuit-field loop does not require Simscape or Simscape Electrical. LTspice support is part of that coupled workflow rather than the top-level purpose by itself:

• bidirectional LTspice conversion between SPICE netlists (.cir, .net) and editable LTspice schematics (.asc);
• a Python-native converter in radia-spice-lab, exposed to MATLAB through a thin wrapper so parsing, symbol dictionaries, and automatic layout have one implementation;
• hidden LTspice execution on Windows, including SSH-driven runs on the LAB and 100号機 development hosts;
• real and complex RAW import, stepped analyses, .noise, and transient FFT APIs;
• a distributable Radia / LTspice Circuit block in the Simulink Library Browser with vector inputs and outputs;
• sampled-data co-simulation that advances LTspice one interval at a time and hands saved node voltages and inductor currents to the next interval;
• a transactional Hysteretic LTspice Plant block that waveform-iterates circuit current, vector Play/Energy hysteresis, flux linkage, and back EMF; failed iterations roll back both circuit and material state, while converged steps output current, flux density, flux linkage, back EMF, Maxwell force, and hysteresis energy;
• MATLAB-native Optuna-style single- and multi-objective studies, parallel Simulink/LTspice trials, and live Pareto monitoring.

% Convert a netlist to an editable schematic and verify connectivity by
% converting it back with LTspice.
schematic = radia.ltspice.netlistToSchematic("plant.cir", ...
    OutputFile="plant.asc", ValidateRoundTrip=true);

% Add a state-handoff LTspice plant with two control inputs and two outputs.
radia.simulink.buildLTspiceBlock("controller_model", ...
    Netlist="plant.cir", ...
    InputNames=["gate"; "reference"], ...
    OutputTraces=["V(out)"; "I(L1)"], ...
    SampleTime_s=1e-4);

The Python circuit converter remains the source of truth; MATLAB and Simulink delegate to it and to the same LTspice execution/RAW contracts. See the MATLAB integration guide and radia-spice-lab documentation.

Hodograph

Hodograph methods turn selected nonlinear magnetic design problems into a linearized design problem in a transformed coordinate space. They provide a direct route to magnetic flux-line and pole-face design, especially for accelerator and precision magnet workflows.

• Clebsch-Hodograph documentation

VIM

The VIM is Radia's successor to magnetic-moment methods. It is not an ELF compatibility layer. Its purpose is to evolve open-boundary magnetic analysis around superposition, linearity, NGSolve-compatible spaces, and practical low-frequency applications.

The current HDiv-VIM path uses NGSolve meshes and finite-element spaces while Radia supplies the electromagnetic charge-Gram and open-boundary operators. This makes the method suitable for soft magnetic materials, nonlinear magnetization workflows, and coupled magnetic applications.

• HDiv-VIM documentation

Eddy

Radia's Eddy framework is a high-order edge-element framework for eddy-current problems. The Eddyable concept identifies basis functions and reduced models that are specialized for the electromagnetic response of a particular problem, reducing the cost of repeated solves without hiding the underlying NGSolve formulation.

• Eddy-current methods

Stream Function

The Stream Function layer supports topology-aware coil and conductor design. It connects field objectives, regularization, contour extraction, and manufacturable single-stroke coil paths to the broader optimization workflow.

• Stream Function documentation

Application layer

The application layer is where the methods become engineering tools.

Workflow	Typical use
Radia Magnet	Permanent magnets, coils, accelerator magnets, and open-space field design
Radia VIM	Magnetic materials, soft iron, nonlinear demagnetization, and coupled magnetics
Radia Eddy	Low-frequency eddy currents, SIBC, ESIM, shielding, and reduced transient response
Radia Stream Function	Coil topology, winding design, field shaping, and optimization
PEEC and circuit workflows	Inductance, resistance, coupling, skin/proximity effects, and SPICE-compatible extraction
Induction heating and motors	Application-specific workflows built from the common Radia and NGSolve layers

Representative application domains include magnetic levitation, wireless power transfer, induction heating, accelerator magnets, motors, printed-circuit conductors, and other open-space magnetic systems.

Respect for NGSolve

NGSolve is not a dependency that Radia wraps casually. It is the numerical language and foundation that Radia builds upon.

Radia follows these principles:

1. Use NGSolve spaces, forms, GridFunctions, and mapped evaluation APIs for finite-element work.
2. Let NGSolve own element orientation, local-to-global transformations, Piola mappings, curved geometry, quadrature, and weak-form assembly.
3. Add Radia-specific physics around those abstractions rather than reimplementing finite-element plumbing in Python.
4. Keep independent analytic, integral, or reduced routes where they improve validation and physical insight.
5. Prefer a clear NGSolve workflow over a Radia-specific parallel vocabulary when NGSolve already provides the right abstraction.

In short: Radia extends NGSolve; it does not compete with it.

Physical scope

Radia targets magneto-quasi-static and Darwin-regime electromagnetic problems. Radia's interaction kernels are Laplace kernels, with surface impedance and skin depth handling frequency-dependent conductor physics. Radia is not a full-wave Helmholtz solver and does not aim to replace full-wave tools for radiation-dominated problems.

The combination is useful when:

• the air region is large or effectively unbounded;
• a magnet or coil moves without wanting to remesh the surrounding air;
• conductor skin depth would make a volume mesh impractical;
• the magnetic source is best represented analytically;
• many parameter variations or optimization steps are required.

AI-native workflow

AI is a first-class user of the platform.

The intended workflow is:

1. An LLM turns an engineering request into a parameterized Python model.
2. MCP tools construct CAD, generate or inspect meshes, and select the appropriate Radia and NGSolve workflow.
3. A headless calculation script runs the solve and writes run.log and result.json.
4. Analytic references, mesh checks, and independent formulations validate the result.
5. Optimization varies the design while preserving the model and its provenance.
6. Simulink provides the operating surface; docs notebooks, Gmsh, or Netgen present the durable result to a human engineer.

Humans remain in the loop for assumptions, physical interpretation, and release decisions. AI automation is valuable because the workflow is executable and inspectable, not because it removes engineering judgment.

radia-mcp

The radia-mcp package provides MCP servers and knowledge tools for the Radia ecosystem. It covers Radia and NGSolve workflows as well as Cubit, Gmsh, build123d, PEEC, optimization, analytical references, Simulink application contracts, and validation.

Install it separately when an MCP client is available:

python -m pip install radia-mcp

Start with the Radia metadata/catalog server to discover the available domain servers and tools. The package README contains client-specific configuration examples.

Integration routes

Need	Preferred route
CAD construction	build123d or Coreform Cubit
Mesh generation	Netgen/NGSolve or Cubit export to Netgen .vol
FEM	NGSolve
BEM and surface operators	ngsolve.bem
Open-boundary source fields	Radia analytical field APIs
Magnetic materials	HDiv-VIM coupled to NGSolve
Eddy currents	NGSolve HCurl workflows, ngsolve.bem, SIBC, ESIM, and reduced models
Field and mesh visualization	Netgen WebGUI and Gmsh/GmshPostExport
Circuit extraction	Radia PEEC and optional radia-spice-lab
Optimization	Python scientific stack, parameter sweeps, and optimization libraries

For Cubit-to-NGSolve workflows, the .vol file is the process boundary: Cubit produces the mesh and the NGSolve/Radia process consumes it. This keeps the Cubit Python 3.10 runtime separate from the Radia/NGSolve Python 3.12 runtime.

Quick start

Supported development target

Component	Target
Operating system	Windows 10/11 or Windows Server
Python	3.12
NGSolve / Netgen	6.2.2604
Coreform Cubit	2025.12, optional
Native build	MSVC + Intel MKL

Install the Python package

For the core Python API and NGSolve integration:

python -m pip install radia

For the optional Cubit mesh-export plugin:

python -m pip install "radia[cubit]"
cubit-plugin-install --verify-only

For AI-assisted workflows:

python -m pip install radia-mcp

Pin all package versions together for a reproducible lab deployment. The release workflow validates the compatibility of radia, cubit-mesh-export, and radia-mcp across the supported machines.

A minimal analytic field

Radia uses SI units. Magnetization is specified in A/m and the field is returned in tesla.

import numpy as np
import radia as rad

mu0 = 4.0 * np.pi * 1e-7
remanence = 1.2  # tesla

magnet = rad.ObjRecMag(
    [0.0, 0.0, 0.0],
    [0.01, 0.01, 0.01],
    [0.0, 0.0, remanence / mu0],
)

field = rad.Fld(magnet, "b", [0.0, 0.0, 0.02])
print("B [T] =", field)
rad.UtiDelAll()

The source field is evaluated analytically. There is no air mesh in this minimal example.

NGSolve source coupling

Radia fields can be supplied to NGSolve as native coefficient functions:

import radia as rad

source_B = rad.RadiaField(magnet, "b")
source_A = rad.RadiaField(magnet, "a")

# Use source_B or source_A in the appropriate NGSolve
# GridFunction, BilinearForm, or LinearForm workflow.

The NGSolve mesh, finite-element space, mapped evaluation, and assembly stay in NGSolve. Radia supplies the electromagnetic source term.

Native NGSolve MEX bridge for MATLAB

Radia also makes selected NGSolve workflows directly usable from MATLAB. The native MEX gateway keeps checked uint64 handles for Mesh, FESpace, CoefficientFunction, GridFunction, BilinearForm, LinearForm, Vector, and Matrix objects. MATLAB can assemble finite-element operators, inspect sparse matrices in NGSolve's global degree-of-freedom ordering, and run HCurl-based reduced and topology workflows without starting a Python process for each operation.

This bridge is valuable in its own right: it lets MATLAB and Simulink users build on NGSolve's mature finite-element foundation while sharing Radia's C++ electromagnetic kernels and artifact contracts. It is not a fork or a reimplementation of NGSolve, and it does not claim to duplicate the complete NGSolve Python API. NGSolve still owns spaces, orientation, transformations, Piola maps, curved geometry, quadrature, assembly, and field evaluation.

The MEX contracts are tested against the corresponding Python/NGSolve results, including complex state/adjoint conventions, matrix layout, and HCurl multifrequency topology gradients. See the MATLAB integration guide for the supported surface, build instructions, and runtime requirements.

Simulink application blocks

The canonical human-facing interfaces are masked blocks in the single Radia Simulink library: Electromagnet, PCB PEEC, Motor, Stream Function, and Induction Heating. Each block delegates to the same DesignSpec and headless calculation used by Python/MCP, always writes run.log and result.json, and adds solver artifacts when execution reaches that stage.

addpath("matlab")
radia.setup()
radia.simulink.buildLibrary()

The initial application-block backend launches the validated Python CLI only on a rising trigger; it does not start Python every simulation step. Promoting an individual application block to a MEX/ROM backend remains optional and requires parity and long-run testing. The native NGSolve MEX bridge above is a separately supported platform capability, not merely a future block backend.

IH temporarily keeps its notebook workbench for an operational comparison:

python -m jupyter lab src/radia/panels/notebooks/radia_ih.ipynb

The Cubit toolbar is a separate, Cubit-embedded integration surface. Normal Radia Python workflows do not install or depend on Cubit's private PySide runtime.

Documentation

• Documentation index
• HDiv-VIM
• Clebsch-Hodograph
• Eddy-current methods
• Stream Function
• PEEC integration
• Induction heating
• Electric machines
• API reference
• Simulink application-block development
• Cubit mesh export
• Build from source
• MCP package and tool catalog

Development

Clone the repository and install the editable packages:

git clone https://github.com/ksugahar/Radia.git
cd Radia
python -m pip install -e .
python -m pip install -e packages/cubit-mesh-export
python -m pip install -e packages/radia-mcp

The native extension is built with MSVC, Intel MKL, CMake, and Ninja:

pwsh -NoProfile -ExecutionPolicy Bypass -File .\Build.ps1 -RadiaOnly

Run focused tests while developing, then broaden the test and validation scope according to the change:

python -m pytest -q tests/test_vim_eddy_hybrid.py
python -m pytest -q

Fast regression tests live under tests/. Important numerical checks and research-grade sweeps live under validation_test/. Result-bearing method notebooks belong under docs/.

Project status

Radia is an active research and engineering platform. The technical foundation is being strengthened before broadening the application surface.

Current priorities:

1. strengthen the platform and its executable validation;
2. complete and document the NGSolve-compatible VIM workflow;
3. establish robust Hodograph design workflows;
4. improve the Eddy and model-reduction framework;
5. consolidate user documentation as the architecture stabilizes.

The repository should lead with analytic solutions, physical methods, and reproducible workflows. Local validation provenance and machine-specific deployment notes stay in the development environment rather than public user-facing material.

Heritage and license

Radia originates from the magnetostatics work developed at the European Synchrotron Radiation Facility. The current project extends that heritage with NGSolve integration, open-boundary engineering methods, high-order workflows, MCP interfaces, and AI-oriented automation.

Radia contains components with different license terms. See LICENSE for the complete terms, including the Radia core, HACApK, and sparsesolv components.