Yuntian Chen

Assistant Professor

Background Information

Yuntian Chen is an assistant professor (Ph.D. Supervisor) at Eastern Institute of Technology, Ningbo. He graduated from the Department of Energy and Power Engineering of Tsinghua University with a dual bachelor’s degree in economics from Peking University. He obtained Ph.D. degree in Energy and Resources Engineering from Peking University with merit. He was a postdoctoral fellow of Peng Cheng Laboratory and the co-founder of RealAI. He has published more than 20 papers, obtained 13 authorized patents, and presided over 6 projects including the National Natural Science Foundation of China (NSFC) Youth Program. His research has been selected as the cover article of Advanced Science. He received the Advances in Applied Energy 2021 Highly Cited Research Paper Award, Young Innovative Talent of Yongjiang Talent Project, Student Award of Merit of Peking University (PKU), Innovation Award of PKU, Excellent Scientific Research Award of PKU, etc.

Google Scholar

Research Field

Scientific machine learning:

Knowledge embedding: By fusing domain knowledge, we construct AI models with physical common sense, improve model accuracy and robustness, reduce data requirements.

Knowledge discovery: We construct AI models that automatically learn scientific laws (e.g., governing equations) from observations and experimental data, and inspire scientific research.

Intelligent energy system:

Energy system, power dispatch, load forecasting, renewable energy power forecasting, etc.

Educational Background

2015.09-2020.01: Ph.D.    Peking University, Energy and Resources Engineering

2011.09-2015.07: B.E.       Tsinghua University, Energy and Power Engineering

2012.05-2015.06: B.Ec.     Peking University, Economics

Work Experience

2020.03-2021.12:Peng Cheng Laboratory, Frontier Research Center, Postdoc

2018.07-2020.02:RealAI, Co-founder

Academic Part-time Jobs (Partial)

2022-present: Young editorial board member of Advances in Applied Energy

Awards and Honors

Young Innovative Talent of Yongjiang Talent Project, 

Advances in Applied Energy 2021 Highly Cited Research Paper Award.

Representative Works

General Information

The researches have been published in academic journals such as Advances in Applied Energy, Geophysical Research Letters, Advanced Science, Journal of Computational Physics, etc. The 10 representative works are:

  • Chen, Y., Luo, Y., Liu, Q., Xu, H., & Zhang, D.* (2022). Symbolic genetic algorithm for discovering open-form partial differential equations (SGA-PDE), Physical Review Research 4, 023174.

  • Xu, H., Chen, Y.*, & Zhang, D.* (2022). Semantic interpretation for convolutional neural networks: What makes a cat a cat? Advanced Science. 2204723.

  • Du, M., Chen, Y.*, & Zhang, D.* (2022). AutoKE: An automatic knowledge embedding framework for scientific machine learning. IEEE Transactions on Artificial Intelligence. 10.1109/TAI.2022.3209167.

  • Chen, Y., Huang, D., Zhang, D.*, Zeng, J., Wang, N., Zhang, H., & Yan, J. (2021). Theory-guided hard constraint projection (HCP): a knowledge-based data-driven scientific machine learning method, Journal of Computational Physics, 445, 110624.

  • Chen, Y., & Zhang, D.* (2021). Theory guided deep-learning for load forecasting (TgDLF) via ensemble long short-term memory, Advances in Applied Energy, 1, 1-15.

  • Chen, Y., & Zhang, D.* (2020). Well log generation via ensemble long short-term memory (EnLSTM) network, Geophysical Research Letters, 47(23), 1-9.

  • Chen, Y., & Zhang, D.* (2020). Physics constrained deep learning of geomechanical logs. IEEE Transactions on Geoscience and Remote Sensing, 58(8), 5932-5943.

  • Chen, Y., Chang, H., Meng, J., & Zhang, D.* (2019). Ensemble Neural Networks (ENN): A gradient-free stochastic method. Neural Networks, 110, 170-185.

  • Zhang, D., Chen, Y.*, & Meng, J. (2018). Synthetic well logs generation via Recurrent Neural Networks. Petroleum Exploration and Development, 45(4), 629-639.

  • Chen, Y., Jiang, S., Zhang, D.*, & Liu, C. (2017). An adsorbed gas estimation model for shale gas reservoirs via statistical learning. Applied Energy, 197, 327-341.


  • Method and system for monitoring fatigue degree of metal parts based on dilated convolution. ZL201911244662.2. 

  • Method and system for degerming measurement uncertainty based on variational autoencoder. ZL201911244603.5. 

  • Method, device, medium and equipment for detecting abnormal data based on time series analysis. ZL201911285902.3.

  • Method, device, medium and equipment for data prediction and completion based on time series analysis. ZL201911284312.9. 

  • Method, device, medium and equipment for processing abnormality of sensor data. ZL202110445193.1. 

  • A kind of data processing method, model training method, device and electronic equipment. ZL202110462699.3. 

  • Data determination method, device and electronic equipment based on spatial information. ZL202110432992.5.

  • Sensing data determination method, device, electronic equipment and readable storage medium. ZL202110421270.X.

  • Training method, parameter adjustment method, and device of parameter adjustment model. CN202110344365.6.

  • Method and device for determining adjustment strategy of process parameters. CN202110497482.6.

  • Optimization method and device for a process parameter adjustment action decision model. CN202110374794.8.

  • Swinging blade turbine. ZL200920175229.3. 

  • Multi-module wave power system with a swinging blade turbine. ZL200920175228.9. 


  • Knowledge embedding and knowledge discovery of machine learning energy forecasting model, National Natural Science Foundation of China (NSFC), 2022-2024

  • Physics-constrained machine learning and its application in petroleum, China Postdoctoral Science Foundation, 2020-2021