MagNet Open Database - maintained by Princeton University
MagNet-AI Platform - maintained by Princeton University
MagNet Toolkit - maintained by Paderborn University
The final winners of the MagNet Challenge 2023 are:
Performance Track :
Innovation Track:
Honorable Mention ($1000):
Software Engineering ($5000):
==================== APEC Ceremony =====================
Here are a few events related to the MagNet Challenge that you may pay attention to at APEC:
If Internet is available, we will try to broadcast the TC10 Meeting and the Award Ceremony on Zoom. Registration Link:
ย | Material A | Material A | Material B | Material B | Material C | Material C | Material D | Material D | Material E | Material E |
---|---|---|---|---|---|---|---|---|---|---|
Team # | % Error | # Size | % Error | # Size | % Error | # Size | % Error | # Size | % Error | # Size |
#1 | 9.6 | 1576 | 5.6 | 1576 | 8.5 | 1576 | 55.3 | 1576 | 13.5 | 1576 |
#2 | 8.5 | 90653 | 2.0 | 90653 | 4.5 | 90653 | 15.9 | 16449 | 8.0 | 16449 |
#3 | 40.5 | 11012900 | 7.8 | 11012900 | 25.2 | 11012900 | 44.1 | 11012900 | 36.3 | 11012900 |
#4 | 4.9 | 8914 | 2.2 | 8914 | 2.9 | 8914 | 20.7 | 8914 | 9.0 | 8914 |
#5 | 16.0 | 2396048 | 3.7 | 2396048 | 6.8 | 2396048 | 201.4 | 2396048 | 19.3 | 2396048 |
#6 | 4.6 | 25923 | 2.8 | 25923 | 6.8 | 25923 | 39.5 | 25923 | 9.3 | 25923 |
#7 | 72.4 | 118785 | 58.0 | 118785 | 66.1 | 118785 | 71.3 | 118785 | 53.7 | 118785 |
#8 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
#9 | 21.3 | 60 | 7.9 | 60 | 14.4 | 60 | 93.9 | 60 | 21.5 | 60 |
#10 | 45.9 | 9728 | 6.9 | 29600 | 26.4 | 21428 | 59.4 | 1740 | 68.4 | 8052 |
#11 | 99.8 | 28564 | 88.7 | 28564 | 93.7 | 28564 | 99.3 | 28564 | 97.8 | 28564 |
#12 | 19.9 | 86728 | 7.4 | 86728 | 7.7 | 86728 | 65.9 | 86728 | 85.1 | 86728 |
#13 | 4.8 | 1755 | 2.2 | 1755 | 3.4 | 1755 | 22.2 | 1755 | 6.6 | 1755 |
#14 | 32.1 | 610 | 33.4 | 760 | 27.7 | 748 | 47.1 | 700 | 28.5 | 610 |
#15 | 351.2 | 329537 | 138.7 | 329537 | 439.5 | 329537 | 810.1 | 329537 | 152.8 | 329537 |
#16 | 38.8 | 81 | 6.9 | 56 | 21.0 | 61 | 50.5 | 23 | 28.2 | 53 |
#17 | 26.1 | 139938 | 12.9 | 139938 | 15.6 | 139938 | 79.1 | 139938 | 19.1 | 139938 |
#18 | 10.0 | 1084 | 3.7 | 1084 | 5.0 | 1084 | 30.7 | 1084 | 19.9 | 1084 |
#19 | 24.5 | 1033729 | 8.0 | 1033729 | 8.9 | 1033729 | 67.9 | 276225 | 118.7 | 1033729 |
#20 | 13.1 | 116061 | 6.4 | 116061 | 9.3 | 116061 | 29.9 | 116061 | 25.7 | 116061 |
#21 | 7.2 | 1419 | 1.9 | 2197 | 3.5 | 2197 | 29.6 | 1419 | 9.1 | 2454 |
#22 | 15.6 | 23000 | 4.3 | 23000 | 9.3 | 23896 | 79.2 | 32546 | 98.0 | 25990 |
#23 | 12.4 | 17342 | 3.8 | 17342 | 10.7 | 17342 | 30.0 | 17342 | 14.1 | 17342 |
#24 | 15.5 | 4285 | 6.1 | 4285 | 10.1 | 4285 | 67.9 | 4285 | 77.0 | 4285 |
========================================================
On November 10th, 2023 - We have received 27 entries for the pre-test. If your team has submitted a pre-test report but was not labeled as [pretest] below, please let us know. Feel free to submit the results to conferences and journals, or seek IP protection. If you used MagNet data, please acknowledge the MagNet project by citing the papers listed at the end of this page.
On November 10th, 2023 โ Data released for final evaluation:
1) Download the new training data and testing data from the following link for 5 new materials similar or different from the previous 10 materials: MagNet Challenge Final Test Data 2) Train, tune, and refine your model or algorithm using the training data. 3) Predict the core losses for all the data points contained in the testing data for the 5 materials. For each material, the prediction results should be formatted into a CSV file with a single column of core loss values. Please make sure the index of these values is consistent with the testing data, so that the evaluation can be conducted correctly.
On December 31st, 2023 โ Final submission: 1) Prediction results for the testing data are due as 5 separate CSV files for the 5 materials. 2) For each material, package your best model as an executable MATLAB/Python function as P=function(B,T,f). This function should be able to directly read the original (B,T,f) CSV files and produce the predicted power P as a CSV file with a single column. For initial evaluation, you donโt need to show how these models were trained/created but only show us the completed models. For final code-evaluation and winner selection, we may ask you to demonstrate how these models were trained/created. 3) A 5-page IEEE TPEL format document due as a PDF file. Please briefly explain the key concepts. 4) The authors listed on the 5-page report will be used as the final team member list. 5) Report the total number of model parameters, as well as your model size as a table in the document. These numbers will be confirmed during the code review process. 6) Full executable model due as a ZIP file for a potential code review with winning teams. These models should be fully executable on a regular personal computer without internet access after installing necessary packages. 7) Submit all the above required files to pelsmagnet@gmail.com.
January to March 2024 โ Model Performance Evaluation, Code Review, Final Winner Selection: 1) We will first evaluate the CSV core loss testing results for the 5 materials. 2) 10 to 15 teams with outstanding performance will be invited for a final code review with brief presentation. These online code review meetings are open to all participating teams. 3) Evaluation criteria: high model accuracy; compact model size; good model readability. 4) The final winners will be selected by the judging committee after jointly considering all the judging factors. 5) All data, models, and results will be released to public, after the winners are selected. 6) Our ultimate goal is to combine the best models from this competition to develop a โstandardโ datasheet model for each of the 10+5 materials.
Criteria for code review: We hope the teams can convince us the developed method is universally applicable to lots of materials and can โautomaticallyโ or โsemi-automaticallyโ produce an accurate and compact model for a new material without too much human interaction, so that we can quickly/automatically reproduce models for a large amount of new materials, as long as data is available. Ultimately, the winning method can become a standard way of training data-driven models for power magnetics, after a community effort of improving it.
========================================================
Rank | Team | 3C90 | 3C94 | 3E6 | 3F4 | 77 | 78 | N27 | N30 | N49 | N87 | Average |
---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | KULeuven | 2.00% | 2.00% | 1.50% | 2.00% | 2.00% | 4.00% | 3.50% | 1.50% | 2.00% | 2.00% | 2.25% |
2 | Fuzhou | 2.69% | 2.50% | 1.20% | 6.00% | 2.37% | 3.18% | 2.03% | 1.31% | 5.46% | 2.13% | 2.89% |
3 | NEU | 2.17% | 2.15% | 3.55% | 4.81% | 4.46% | 3.13% | 2.69% | 3.06% | 5.23% | 2.38% | 3.36% |
4 | TUDelft | 3.57% | 2.79% | 1.64% | 8.81% | 3.40% | 3.95% | 3.23% | 1.70% | 8.87% | 2.84% | 4.08% |
5 | Bristol | 3.68% | 2.77% | 1.64% | 7.66% | 3.09% | 3.07% | 2.53% | 8.63% | 7.96% | 2.63% | 4.37% |
6 | XJTU | 3.99% | 3.71% | 2.28% | 8.88% | 4.50% | 4.64% | 4.84% | 2.52% | 8.88% | 4.20% | 4.84% |
7 | Paderborn | 6.52% | 5.29% | 2.41% | 8.79% | 5.74% | 5.12% | 5.07% | 3.34% | 9.48% | 5.38% | 5.71% |
8 | HDU | 6.38% | 5.65% | 1.56% | 11.39% | 4.77% | 5.65% | 5.33% | 1.60% | 10.36% | 4.77% | 5.75% |
9 | NJUPT | 7.22% | 6.08% | 5.84% | 11.64% | 8.32% | 8.98% | 8.17% | 5.60% | 12.53% | 6.23% | 8.06% |
10 | ASU | 6.18% | 5.65% | 4.33% | 19.98% | 6.30% | 6.19% | 6.16% | 6.37% | 16.15% | 5.67% | 8.30% |
11 | SEU 2 | 10.83% | 8.79% | 4.42% | 27.02% | 12.18% | 10.86% | 7.54% | 5.88% | 14.88% | 7.99% | 11.04% |
12 | Sydney | 12.25% | 9.59% | 4.33% | 23.46% | 8.74% | 9.61% | 8.77% | 4.32% | 26.32% | 9.89% | 11.73% |
13 | IISC | 7.89% | 22.04% | 12.25% | 12.32% | 12.29% | 11.27% | 17.02% | 14.50% | 10.62% | 13.10% | 13.33% |
14 | PoliTo | 14.18% | 18.67% | 7.25% | 16.12% | 14.48% | 10.82% | 8.63% | 14.07% | 13.48% | 16.40% | 13.41% |
15 | Boulder | 19.93% | 14.78% | 3.34% | 12.23% | 15.81% | 16.21% | 18.13% | 4.70% | 19.54% | 22.42% | 14.71% |
16 | Tsinghua | 17.94% | 11.54% | 10.74% | 17.43% | 9.90% | 19.85% | 19.61% | 13.96% | 21.72% | 8.70% | 15.14% |
17 | ZJU-UIUC | 20.52% | 11.44% | 9.62% | 26.34% | 18.94% | 19.54% | 8.80% | 10.05% | 18.09% | 14.04% | 15.74% |
18 | UTK | 16.87% | 14.70% | 6.82% | 28.23% | 10.40% | 13.57% | 13.84% | 5.68% | 52.80% | 11.48% | 17.44% |
19 | Tribhuvan | 10.58% | 12.10% | 23.42% | 9.23% | 17.66% | 22.17% | 24.23% | 18.22% | 24.60% | 15.50% | 17.77% |
20 | ZJU | 25.50% | 13.97% | 60.47% | 13.00% | 19.90% | 13.94% | 12.48% | 5.02% | 19.23% | 26.56% | 21.01% |
21 | Mondragon | 29.26% | 24.38% | 22.32% | 28.58% | 29.60% | 30.43% | 30.27% | 21.29% | 36.36% | 27.83% | 28.03% |
22 | Purdue | 38.74% | 29.91% | 29.69% | 53.67% | 35.16% | 49.64% | 30.83% | 33.33% | 39.70% | 30.73% | 37.14% |
23 | NTUT | 48.58% | 46.61% | 23.99% | 112.10% | 49.45% | 49.45% | 41.13% | 19.58% | 173.50% | 32.91% | 59.73% |
24 | SAL | 26.28% | 19.17% | 4.08% | 34.94% | 15.06% | 20.07% | 20.07% | 7.47% | 21.67% | 1861.12% | 202.99% |
25 | Utwente | 968.79% | 436.58% | 313.66% | 141.77% | 290.70% | 332.79% | 1431.70% | 360.66% | 110.12% | 506.80% | 489.36% |
ย | Average | 52.50% | 29.31% | 22.49% | 25.86% | 24.21% | 27.13% | 69.46% | 22.97% | 27.58% | 105.75% | 40.73% |
========================================================
On November 10th, a preliminary test result is due to evaluate your already developed models for the 10 materials:
Step 1: Download the MagNet Challenge Validation Data for the 10 existing materials each consisting of 5,000 randomly sampled data from the original database.
Step 2: Use this database to evaluate your already-trained models.
Step 3: Report your results following the provided Template. Zip your Models and Results and send them to pelsmagnet@gmail.com.
We will use relative error to evaluate your models (the absolute error between the predicted and measured values).
$Percent\ Relative\ Error = \frac{\left | meas-pred \right | }{meas}\cdot100$ \%, where $meas$ is MagNetโs Core Loss measurement and $pred$ is the model prediction. |
The purpose of the preliminary test is to get you familiar with the final testing process. The preliminary test results have nothing to do with the final competition results.
** In the final test, we will provide a small or large dataset for training, and a small or large dataset for testing. The training and testing data for different materials may be offered in different ways to test the modelโs performance from different angles. **
The judging committee will evaluate the results of each team with the following criterias.