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MagNet Challenge 2023

IEEE PELS-Google-Enphase-Princeton MagNet Challenge

This site provides the latest information about the MagNet Challenge.

Please contact [email protected] for all purposes.

**We reorganized the datasets and tools used in the MagNet Challenge and moved them to the following sites

MagNet Open Database - maintained by Princeton University

MagNet-AI Platform - maintained by Princeton University

MagNet Toolkit - maintained by Paderborn University

**We marked the completion of the MagNet Challenge 2023 by hosting an Award Ceremony at APEC 2024. The Award Ceremony was well attended with ~100 student participants and audiences.

**More information will be made available on the PELS website, together with information for tranferring the prize money. We are still in the process of tranferring the prize money between Princeton and IEEE. We will be in touch in a few weeks!

APEC Pictures

APEC Slides

Award Certificates

Organizing Certificates

Challenge Summary

Final Reports

Submitted Models

Final Presentations

The final winners of the MagNet Challenge 2023 are:

Performance Track :

  • 1st ($10000) Paderborn University, Paderborn, Germany ๐Ÿ‡ฉ๐Ÿ‡ช
  • 2nd ($5000) Fuzhou University, Fuzhou, China ๐Ÿ‡จ๐Ÿ‡ณ
  • 3rd ($3000) University of Bristol, Bristol, UK ๐Ÿ‡ฌ๐Ÿ‡ง

Innovation Track:

  • 1st ($10000) University of Sydney, Sydney, Australia ๐Ÿ‡ฆ๐Ÿ‡บ
  • 2nd ($5000) TU Delft, Delft, Netherland ๐Ÿ‡ณ๐Ÿ‡ฑ
  • 3rd ($3000) Mondragon University, Hernani, Spain ๐Ÿ‡ช๐Ÿ‡ธ

Honorable Mention ($1000):

  • Arizona State University, Tempe AZ, USA ๐Ÿ‡บ๐Ÿ‡ธ
  • Indian Institute of Science, Bangalore, India ๐Ÿ‡ฎ๐Ÿ‡ณ
  • Xi'an Jiaotong University, Xi'an, China ๐Ÿ‡จ๐Ÿ‡ณ
  • Zhejiang University-UIUC, Hangzhou, China ๐Ÿ‡จ๐Ÿ‡ณ
  • University of Tennessee, Knoxville, USA ๐Ÿ‡บ๐Ÿ‡ธ
  • Politecnico di Torino, Torino, Italy ๐Ÿ‡ฎ๐Ÿ‡น
  • Southeast University Team 1, Nanjing, China ๐Ÿ‡จ๐Ÿ‡ณ
  • Southeast University Team 2, Nanjing, China ๐Ÿ‡จ๐Ÿ‡ณ
  • Tsinghua University, Beijing, China ๐Ÿ‡จ๐Ÿ‡ณ

Software Engineering ($5000):

  • University of Sydney, Sydney, Australia ๐Ÿ‡ฆ๐Ÿ‡บ

==================== APEC Ceremony =====================

We will host a MagNet Challenge Award Ceremony on Wednesday Feb 28, 4:30pm-5:30pm PCT during APEC 2024 in Long Beach, California at Hyatt Regency Ballroom DEF . We look forward to seeing many of you there (and on Zoom) to celebrate what we have done and what we plan to do in the future!

Here are a few events related to the MagNet Challenge that you may pay attention to at APEC:

  • Saturday 2/24: Magnetics Workshop. Stop at Haoran and Shukai's poster to share about what we have learnt from MagNet Challenge 2023: https://www.psma.com/technical-forums/magnetics/workshop.
  • Tuesday 2/27: PELS TC10 Meeting, 12:30pm-2:00pm PST (updated), Hyatt Regency Hotel, Seaview A. We will discuss and plan the logistics for MagNet Challenge 2024. Join this event and share your opinions if you cannot attend the Award Ceremony on Wednesday. https://apec-conf.org/special-events/pels-2024.
  • Wednesday 2/28: MagNet Challenge Award Ceremony, 4:30pm-5:30pm PST, Hyatt Regency Ballroom DEF. We will announce the winners, celebrate what we have done in the past year, and plan for MagNet Challenge 2024, and chat and make new friends. https://apec-conf.org/special-events/pels-2024.

If Internet is available, we will try to broadcast the TC10 Meeting and the Award Ceremony on Zoom. Registration Link:

**Download the Final Evaluation Kit for Self Evaluation of Model Accuracy

Register for the Code Review Town Hall Meeting, please email us your preferred slot. All teams are welcome to present, listen, and discuss. Since we were able to execute most codes, we will not host individual code review meetings.

  • Session #1 (Jan 17, Wed) Teams (8 slots): SAL, Tribhuvan, Bristol
  • Session #2 (Jan 18, Thu) Teams (8 slots): ZJUI, Paderborn, Tsinghua, NTUT, Mondragon, SEU-MC, HDU, CU-Boulder
  • Session #3 (Jan 19, Fri) Teams (8 slots): KU Leuven, Sydney, SEU-WX, PoliTO, UTK, Fuzhou, TUDelft, IISc

Preliminary Evaluation Results for the Final Submission

  • Model Error is evaluated as the average absolute 95th percentile error of the core loss prediction.
  • Model Size is evaluated as the number of parameters that the model needs to remember to predict the core loss of each material.
  • Let us know if you find any discrepancy.
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

**Download the Final Submission Template Here (finaltest/TeamName.zip)

========================================================

**We have received the final submission from the following teams. If your team have submitted your results but is not listed here, please let us know immediately.

  • Arizona State University, Tempe AZ, USA ๐Ÿ‡บ๐Ÿ‡ธ
  • Fuzhou University, Fuzhou, China ๐Ÿ‡จ๐Ÿ‡ณ
  • Hangzhou Dianzi University, Hangzhou, China ๐Ÿ‡จ๐Ÿ‡ณ
  • Indian Institute of Science, Bangalore, India ๐Ÿ‡ฎ๐Ÿ‡ณ
  • KU Leuven, Leuven, Belgium ๐Ÿ‡ง๐Ÿ‡ช
  • Mondragon University, Hernani, Spain ๐Ÿ‡ช๐Ÿ‡ธ
  • Nanjing University of Posts and Telecom., Nanjing, China ๐Ÿ‡จ๐Ÿ‡ณ
  • Nanyang Technological University, Singapore ๐Ÿ‡ธ๐Ÿ‡ฌ
  • National Taipei University of Technology, Taipei, Taiwan ๐Ÿ‡น๐Ÿ‡ผ
  • Paderborn University, Paderborn, Germany ๐Ÿ‡ฉ๐Ÿ‡ช
  • Politecnico di Torino, Torino, Italy ๐Ÿ‡ฎ๐Ÿ‡น
  • Silicon Austria Labs, Graz, Austria ๐Ÿ‡ฆ๐Ÿ‡น
  • Southeast University Team 1, Nanjing, China ๐Ÿ‡จ๐Ÿ‡ณ
  • Southeast University Team 2, Nanjing, China ๐Ÿ‡จ๐Ÿ‡ณ
  • Tribhuvan University, Lalitpur, Nepal ๐Ÿ‡ณ๐Ÿ‡ต
  • Tsinghua University, Beijing, China ๐Ÿ‡จ๐Ÿ‡ณ
  • TU Delft, Delft, Netherland ๐Ÿ‡ณ๐Ÿ‡ฑ
  • University of Bristol, Bristol, UK ๐Ÿ‡ฌ๐Ÿ‡ง
  • University of Colorado Boulder, Boulder CO, USA ๐Ÿ‡บ๐Ÿ‡ธ
  • University of Manchester, Manchester, UK ๐Ÿ‡ฌ๐Ÿ‡ง
  • University of Sydney, Sydney, Australia ๐Ÿ‡ฆ๐Ÿ‡บ
  • University of Tennessee, Knoxville, USA ๐Ÿ‡บ๐Ÿ‡ธ
  • Xi'an Jiaotong University, Xi'an, China ๐Ÿ‡จ๐Ÿ‡ณ
  • Zhejiang University-UIUC, Hangzhou, China ๐Ÿ‡จ๐Ÿ‡ณ

MagNet Challenge 2023 Office Hour Registration Link

  • 11-30-2023 MagNet Challenge Office Hour #1 Video PDF

MagNet Challenge 2023 Final Evaluation Rules Here:

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 don't 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 [email protected].

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 "standard" 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 "automatically" or "semi-automatically" 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.

========================================================

Self-reported pre-test results from 25 teams ranked based on 95-Prct-Error.

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%
Average 52.50% 29.31% 22.49% 25.86% 24.21% 27.13% 69.46% 22.97% 27.58% 105.75% 40.73%

========================================================

[Past] MagNet Challenge 2023 Pretest Evaluation Rules Here:

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 [email protected].

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 MagNet's 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 model's performance from different angles. ***

MagNet Challenge Timeline

  • 02-01-2023 MagNet Challenge Handbook Released PDF
  • 03-21-2023 Data Quality Report PDF
  • 04-01-2023 Data for 10 Materials Available Dropbox
  • 05-15-2023 1-Page Letter of Intent Due with Signature PDF
  • 06-15-2023 2-Page Concept Proposal Due PDF DOC Latex
  • 07-01-2023 Notification of Acceptance (all 39 teams accepted)
  • 08-01-2023 Expert Feedback on the Concept Proposal
  • Teams develop a semi/fully-automated software pipeline to process data and generate models for 10 materials
  • 11-10-2023 Preliminary Submission Due (postponed from 11-01-2023)
  • Teams use the previously developed software pipeline to process new data and generate models for 3 new materials
  • 12-31-2023 Final Submission Due (postponed from 12-24-2023)
  • 02-29-2024 APEC 2024 - Winner Announcement and Presentation

Evaluation Timeline

  • 06-15-2023 Evaluate the concept proposals and ensure all teams understand the competition rules.
  • 11-10-2023 Evaluate the 10 models the teams developed for the 10 materials and provide feedback for improvements.
  • 12-31-2023 Evaluate the 3 new models the teams developed for the 3 new materials and announce the winners.

Evaluation Criterias

The judging committee will evaluate the results of each team with the following criterias.

  • Model accuracy (30%): core loss prediction accuracy evaluated by 95th percentile error (lower error better)
  • Model size (30%): number of parameters the model needs to store for each material (smaller model better)
  • Model explanability (20%): explanability of the model based on existing physical insights (more explainable better)
  • Model novelty (10%): new concepts or insights presented by the model (newer insights better)
  • Software quality (10%): quality of the software engineering (more concise better)

MagNet Webinar Recordings

  • 04-07-2023 MagNet Webinar Series #1 - Kickoff Meeting Video PDF
  • 05-12-2023 MagNet Webinar Series #2 - Equation-based Method Video PDF
  • 05-19-2023 MagNet Webinar Series #3 - Machine Learning Method Video PDF
  • 05-26-2023 MagNet Webinar Series #4 - Data Complexity and Quality Video PDF

MagNet Challenge Discussions

  • MagNet GitHub Discussion Forum Link

MagNet Baseline Tools and Tutorials

  • MagNet: Equation-based Baseline Models - by Dr. Thomas Guillod (Dartmouth) Link
  • MagNet: Machine Learning Tutorials - by Haoran Li (Princeton) Link
  • MagNet: Data Processing Tools - by Dr. Diego Serrano (Princeton) Link

MagNet Challenge Awards

  • Model Performance Award, First Place $10,000
  • Model Performance Award, Second Place $5,000
  • Model Novelty Award, First Place $10,000
  • Model Novelty Award, Second Place $5,000
  • Outstanding Software Engineering Award $5,000
  • Honorable Mentions Award multiple x $1,000

Participating Teams (05-20-2023) 40 Teams from 17 Countries and Regions

Denmark, USA, Brazil, China, India, Belgium, Spain, Singapore, Taiwan, Germany, Italy, South Korea, Austria, Nepal, Netherland, UK, Australia

All 39 Concept Papers have been Received !!!

$30,000 budget from IEEE PELS confirmed!

$10,000 gift from Google received!

$10,000 gift from Enphase received!

  • Aalborg University, Aalborg, Denmark ๐Ÿ‡ฉ๐Ÿ‡ฐ
  • Arizona State University, Tempe AZ, USA ๐Ÿ‡บ๐Ÿ‡ธ - [pretest]
  • Cornell University Team 1, Ithaca, USA ๐Ÿ‡บ๐Ÿ‡ธ
  • Cornell University Team 2, Ithaca, USA ๐Ÿ‡บ๐Ÿ‡ธ
  • Federal University of Santa Catarina, Florianopolis, Brazil ๐Ÿ‡ง๐Ÿ‡ท - [pretest]
  • Fuzhou University, Fuzhou, China ๐Ÿ‡จ๐Ÿ‡ณ - [pretest]
  • Hangzhou Dianzi University, Hangzhou, China ๐Ÿ‡จ๐Ÿ‡ณ - [pretest]
  • Indian Institute of Science, Bangalore, India ๐Ÿ‡ฎ๐Ÿ‡ณ - [pretest]
  • Jinan University, Guangzhou, China ๐Ÿ‡จ๐Ÿ‡ณ
  • KU Leuven, Leuven, Belgium ๐Ÿ‡ง๐Ÿ‡ช - [pretest]
  • Mondragon University, Hernani, Spain ๐Ÿ‡ช๐Ÿ‡ธ - [pretest]
  • Nanjing University of Posts and Telecom., Nanjing, China ๐Ÿ‡จ๐Ÿ‡ณ - [pretest]
  • Nanyang Technological University, Singapore ๐Ÿ‡ธ๐Ÿ‡ฌ
  • National Taipei University of Technology, Taipei, Taiwan ๐Ÿ‡น๐Ÿ‡ผ - [pretest]
  • Northeastern University, Boston MA, USA ๐Ÿ‡บ๐Ÿ‡ธ - [pretest]
  • Paderborn University, Paderborn, Germany ๐Ÿ‡ฉ๐Ÿ‡ช - [pretest]
  • Politecnico di Torino, Torino, Italy ๐Ÿ‡ฎ๐Ÿ‡น - [pretest]
  • Princeton University, Princeton NJ, USA ๐Ÿ‡บ๐Ÿ‡ธ (not competing)
  • Purdue University, West Lafayette IN, USA ๐Ÿ‡บ๐Ÿ‡ธ - [pretest]
  • Seoul National University, Seoul, South Korea ๐Ÿ‡ฐ๐Ÿ‡ท
  • Silicon Austria Labs, Graz, Austria ๐Ÿ‡ฆ๐Ÿ‡น - [pretest]
  • Southeast University Team 1, Nanjing, China ๐Ÿ‡จ๐Ÿ‡ณ - [pretest]
  • Southeast University Team 2, Nanjing, China ๐Ÿ‡จ๐Ÿ‡ณ - [pretest]
  • Tribhuvan University, Lalitpur, Nepal ๐Ÿ‡ณ๐Ÿ‡ต - [pretest]
  • Tsinghua University, Beijing, China ๐Ÿ‡จ๐Ÿ‡ณ - [pretest]
  • TU Delft, Delft, Netherland ๐Ÿ‡ณ๐Ÿ‡ฑ - [pretest]
  • University of Bristol, Bristol, UK ๐Ÿ‡ฌ๐Ÿ‡ง - [pretest]
  • University of Colorado Boulder, Boulder CO, USA ๐Ÿ‡บ๐Ÿ‡ธ - [pretest]
  • University of Kassel, Kassel, Germany ๐Ÿ‡ฉ๐Ÿ‡ช
  • University of Manchester, Manchester, UK ๐Ÿ‡ฌ๐Ÿ‡ง
  • University of Nottingham, Nottingham, UK ๐Ÿ‡ฌ๐Ÿ‡ง - [pretest]
  • University of Sydney, Sydney, Australia ๐Ÿ‡ฆ๐Ÿ‡บ - [pretest]
  • University of Tennessee, Knoxville, USA ๐Ÿ‡บ๐Ÿ‡ธ - [pretest]
  • University of Twente Team 1, Enschede, Netherland ๐Ÿ‡ณ๐Ÿ‡ฑ - [pretest]
  • University of Twente Team 2, Enschede, Netherland ๐Ÿ‡ณ๐Ÿ‡ฑ
  • University of Wisconsin-Madison, Madison MI, USA ๐Ÿ‡บ๐Ÿ‡ธ
  • Universidad Politรฉcnica de Madrid, Madrid, Spain ๐Ÿ‡ช๐Ÿ‡ธ
  • Xi'an Jiaotong University, Xi'an, China ๐Ÿ‡จ๐Ÿ‡ณ - [pretest]
  • Zhejiang University, Hangzhou, China ๐Ÿ‡จ๐Ÿ‡ณ - [pretest]
  • Zhejiang University-UIUC, Hangzhou, China ๐Ÿ‡จ๐Ÿ‡ณ - [pretest]

Related Websites

MagNet Project Reference Papers

  • D. Serrano et al., "Why MagNet: Quantifying the Complexity of Modeling Power Magnetic Material Characteristics," in IEEE Transactions on Power Electronics, doi: 10.1109/TPEL.2023.3291084. Paper
  • H. Li et al., "How MagNet: Machine Learning Framework for Modeling Power Magnetic Material Characteristics," in IEEE Transactions on Power Electronics, doi: 10.1109/TPEL.2023.3309232. Paper
  • H. Li, D. Serrano, S. Wang and M. Chen, "MagNet-AI: Neural Network as Datasheet for Magnetics Modeling and Material Recommendation," in IEEE Transactions on Power Electronics, doi: 10.1109/TPEL.2023.3309233. Paper

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