Publish verified MagNet legacy Transformer models

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---
license: mit
library_name: onnx
language: en
tags:
  - magnetics
  - core-loss
  - b-h-loop
  - transformer
  - onnx
  - experimental
---
# MagNet legacy B–H Transformers (ONNX preview)

These are the ten material-specific steady-state B–H Transformer checkpoints
from the original Princeton MagNet application, converted to fixed-shape ONNX
encoder and decoder graphs. The release is a **numerical port of the legacy
inference**, not a newly trained model or an independently certified accuracy
benchmark. It does not contain a transient neural model.

The available materials are **3C90, 3C94, 3E6, 3F4, 77, 78, N27, N30, N49,
and N87**. There are no legacy Transformer checkpoints for the other five
materials in the public 15-material steady-state dataset.

## Files and inference contract

Each material folder contains `encoder.onnx`, `decoder.onnx`, `norm.json`, and
`reference.json`. These ONNX files and JSON normalizers are the preferred
inference artifacts. `pytorch/Model_<material>_Transformer.sd` and
`pytorch/Norm_<material>.pt` are the original, unmodified PyTorch weights and
normalizers, included for provenance and reproducibility; they are **not**
needed for ONNX inference. If you inspect them with PyTorch, use
`torch.load(path, map_location="cpu", weights_only=True)` and verify the
SHA-256 values in `reference.json`. Do not use unrestricted pickle loading.
`domain/hull_<material>.npy` stores the corresponding measured-data
operating-domain hull equations. `predict.py` is a standalone CPU example
using NumPy and ONNX Runtime; see `requirements.txt`.

- Input: 128 finite, uniformly sampled magnetic flux-density values **B in T**
  over one cycle, without a repeated closing sample; frequency **f in Hz**
  (strictly positive); temperature **°C**; and DC magnetic field bias **A/m**.
- `norm.json` contains ten float32-origin values. Normalize B with
  `(B - n[0]) / n[1]`, `log10(f)` with `(log10(f) - n[2]) / n[3]`, temperature
  with `(temperature - n[4]) / n[5]`, and DC bias with `(bias - n[6]) / n[7]`.
- The encoder takes `source` float32 `[1,128,1]` and `variables` float32
  `[1,3]` in `[log10(f), temperature, bias]` order after normalization. Its
  `memory` output is `[1,128,24]`.
- Initialize decoder `target` float32 `[1,129,1]` to zero and set its first
  value to `0.1`. Call the decoder 128 times, setting target value `t` to
  decoder output value `t-1` for `t=1…128`; then call it once more. The
  decoder takes `memory` and `target`; its `output` is `[1,129,1]`.
- Output: the first 128 decoder outputs give **H in A/m** after
  `H = output * n[9] + n[8]`. The signed volumetric loop integral is
  `f * trapezoid(H, B)` in **W/m³**, using the 127 adjacent sample intervals.
  This reproduces the historical implementation; it does not add a synthetic
  closing edge. A negative signed result should be inspected, not silently
  converted to a positive loss.

The provided sine and triangle generator follows the original 128-point
waveform convention. The triangle duty ratio is the normalized time of its
positive B peak and must be strictly between zero and one. A custom 128-point
B waveform is also accepted; the measured-data hull test is then left
undetermined because the original hull uses an ideal-waveform duty coordinate.

## Operating domain and limitations

The historical hull is a five-dimensional convex hull of measured operating
points in this order: `[frequency_Hz, peak_AC_flux_T, DC_bias_A_per_m,
temperature_C, duty_ratio]`. Each row of `domain/hull_<material>.npy` is an
inequality `[a0,a1,a2,a3,a4,b]` with `a·point + b <= 1e-10` for an in-hull
point. For ideal sine waves the duty coordinate is `0.5`; for triangular waves
it is the specified duty ratio. A hull pass indicates only that the operating
point lies within this *geometric* domain; it is not an accuracy guarantee.
The examples still return a prediction outside the hull and flag it.
All ten nominal sine-wave packages passed local PyTorch-to-ONNX numerical
parity tests; the largest relative difference in predicted loss was
`8.71e-7`. N87 was also checked at seven sine/triangle operating points,
including 30% and 70% triangle duty, 25–90 °C, 0/30 A/m DC bias, and two
frequencies; the largest observed relative difference was `1.50e-6`. A
Chrome/ONNX Runtime Web smoke test passed the ten reference cases, with a
largest relative difference from PyTorch of `7.66e-7`. Per-material
`reference.json` includes one PyTorch B/H reference waveform, parity result,
and SHA-256 hashes of the source checkpoint, normalizer, and architecture.
Four N87 measurements were compared at recorded operating points as a
**sanity check only** using generated ideal waveforms. They may overlap the
original training data. The original training split and full provenance have
not been reconstructed, and no independent generalization metric is claimed.
These models must not be treated as validated material datasheets, especially
for extrapolated operating points. The related public data are available at
[minjiechen/MagNet](https://huggingface.co/datasets/minjiechen/MagNet), but
their present release files have **not** been established as the exact
training set for these legacy checkpoints.
## Quick start
With Python 3.12 and the packages in `requirements.txt` installed:
```bash
python predict.py --material N87 --waveform sine --frequency-hz 100000 \
  --flux-t 0.1 --temperature-c 25 --bias-a-per-m 0
python predict.py --material N87 --waveform triangle --duty 0.3 \
  --frequency-hz 100000 --flux-t 0.1 --temperature-c 25
```
Use `--b-json path/to/waveform.json` instead of `--waveform` and `--flux-t`
for a JSON array of 128 B values in T. Do not execute model files from
untrusted sources. The example does not load the original PyTorch archives.
## Provenance, license, and citation
The original architecture, weights, and hulls come from the
[Princeton MagNet source repository](https://github.com/PrincetonUniversity/magnet),
under its MIT license (copyright 2021 Minjie Chen). The converted ONNX files
preserve that license; the related public measurement datasets have a
**separate CC BY 4.0 license**. Maintainer: Princeton Power Electronics
Research Lab / Minjie Chen.
Please cite: H. Li et al., “How MagNet: Machine Learning Framework for
Modeling Power Magnetic Material Characteristics,” *IEEE Transactions on Power
Electronics* (2023), [doi:10.1109/TPEL.2023.3309232](https://doi.org/10.1109/TPEL.2023.3309232).

	---
	license: mit
	library_name: onnx
	language: en
	tags:
	- magnetics
	- core-loss
	- b-h-loop
	- transformer
	- onnx
	- experimental
	---

	# MagNet legacy B–H Transformers (ONNX preview)

	These are the ten material-specific steady-state B–H Transformer checkpoints
	from the original Princeton MagNet application, converted to fixed-shape ONNX
	encoder and decoder graphs. The release is a **numerical port of the legacy
	inference**, not a newly trained model or an independently certified accuracy
	benchmark. It does not contain a transient neural model.

	The available materials are **3C90, 3C94, 3E6, 3F4, 77, 78, N27, N30, N49,
	and N87**. There are no legacy Transformer checkpoints for the other five
	materials in the public 15-material steady-state dataset.

	## Files and inference contract

	Each material folder contains `encoder.onnx`, `decoder.onnx`, `norm.json`, and
	`reference.json`. These ONNX files and JSON normalizers are the preferred
	inference artifacts. `pytorch/Model_<material>_Transformer.sd` and
	`pytorch/Norm_<material>.pt` are the original, unmodified PyTorch weights and
	normalizers, included for provenance and reproducibility; they are not
	needed for ONNX inference. If you inspect them with PyTorch, use
	`torch.load(path, map_location="cpu", weights_only=True)` and verify the
	SHA-256 values in `reference.json`. Do not use unrestricted pickle loading.
	`domain/hull_<material>.npy` stores the corresponding measured-data
	operating-domain hull equations. `predict.py` is a standalone CPU example
	using NumPy and ONNX Runtime; see `requirements.txt`.

	- Input: 128 finite, uniformly sampled magnetic flux-density values B in T
	over one cycle, without a repeated closing sample; frequency f in Hz
	(strictly positive); temperature °C; and DC magnetic field bias A/m.
	- `norm.json` contains ten float32-origin values. Normalize B with
	`(B - n[0]) / n[1]`, `log10(f)` with `(log10(f) - n[2]) / n[3]`, temperature
	with `(temperature - n[4]) / n[5]`, and DC bias with `(bias - n[6]) / n[7]`.
	- The encoder takes `source` float32 `[1,128,1]` and `variables` float32
	`[1,3]` in `[log10(f), temperature, bias]` order after normalization. Its
	`memory` output is `[1,128,24]`.
	- Initialize decoder `target` float32 `[1,129,1]` to zero and set its first
	value to `0.1`. Call the decoder 128 times, setting target value `t` to
	decoder output value `t-1` for `t=1…128`; then call it once more. The
	decoder takes `memory` and `target`; its `output` is `[1,129,1]`.
	- Output: the first 128 decoder outputs give H in A/m after
	`H = output * n[9] + n[8]`. The signed volumetric loop integral is
	`f * trapezoid(H, B)` in W/m³, using the 127 adjacent sample intervals.
	This reproduces the historical implementation; it does not add a synthetic
	closing edge. A negative signed result should be inspected, not silently
	converted to a positive loss.

	The provided sine and triangle generator follows the original 128-point
	waveform convention. The triangle duty ratio is the normalized time of its
	positive B peak and must be strictly between zero and one. A custom 128-point
	B waveform is also accepted; the measured-data hull test is then left
	undetermined because the original hull uses an ideal-waveform duty coordinate.

	## Operating domain and limitations

	The historical hull is a five-dimensional convex hull of measured operating
	points in this order: `[frequency_Hz, peak_AC_flux_T, DC_bias_A_per_m,
	temperature_C, duty_ratio]`. Each row of `domain/hull_<material>.npy` is an
	inequality `[a0,a1,a2,a3,a4,b]` with `a·point + b <= 1e-10` for an in-hull
	point. For ideal sine waves the duty coordinate is `0.5`; for triangular waves
	it is the specified duty ratio. A hull pass indicates only that the operating
	point lies within this geometric domain; it is not an accuracy guarantee.
	The examples still return a prediction outside the hull and flag it.

	All ten nominal sine-wave packages passed local PyTorch-to-ONNX numerical
	parity tests; the largest relative difference in predicted loss was
	`8.71e-7`. N87 was also checked at seven sine/triangle operating points,
	including 30% and 70% triangle duty, 25–90 °C, 0/30 A/m DC bias, and two
	frequencies; the largest observed relative difference was `1.50e-6`. A
	Chrome/ONNX Runtime Web smoke test passed the ten reference cases, with a
	largest relative difference from PyTorch of `7.66e-7`. Per-material
	`reference.json` includes one PyTorch B/H reference waveform, parity result,
	and SHA-256 hashes of the source checkpoint, normalizer, and architecture.

	Four N87 measurements were compared at recorded operating points as a
	sanity check only using generated ideal waveforms. They may overlap the
	original training data. The original training split and full provenance have
	not been reconstructed, and no independent generalization metric is claimed.
	These models must not be treated as validated material datasheets, especially
	for extrapolated operating points. The related public data are available at
	[minjiechen/MagNet](https://huggingface.co/datasets/minjiechen/MagNet), but
	their present release files have not been established as the exact
	training set for these legacy checkpoints.

	## Quick start

	With Python 3.12 and the packages in `requirements.txt` installed:

	```bash
	python predict.py --material N87 --waveform sine --frequency-hz 100000 \
	--flux-t 0.1 --temperature-c 25 --bias-a-per-m 0
	python predict.py --material N87 --waveform triangle --duty 0.3 \
	--frequency-hz 100000 --flux-t 0.1 --temperature-c 25
	```

	Use `--b-json path/to/waveform.json` instead of `--waveform` and `--flux-t`
	for a JSON array of 128 B values in T. Do not execute model files from
	untrusted sources. The example does not load the original PyTorch archives.

	## Provenance, license, and citation

	The original architecture, weights, and hulls come from the
	[Princeton MagNet source repository](https://github.com/PrincetonUniversity/magnet),
	under its MIT license (copyright 2021 Minjie Chen). The converted ONNX files
	preserve that license; the related public measurement datasets have a
	separate CC BY 4.0 license. Maintainer: Princeton Power Electronics
	Research Lab / Minjie Chen.

	Please cite: H. Li et al., “How MagNet: Machine Learning Framework for
	Modeling Power Magnetic Material Characteristics,” *IEEE Transactions on Power
	Electronics* (2023), [doi:10.1109/TPEL.2023.3309232](https://doi.org/10.1109/TPEL.2023.3309232).