Hi, my name is Yizheng Wang. I will be a Ph.D. student in Department of Engineering Mechanics, Tsinghua University (2024), supervised by Prof. Yinghua Liu and Prof. Rabczuk. I major in engineering mechanics, mainly doing computational solid mechanics and received my master’s degree supervised by Prof. Yinghua Liu (2019-2022) from the Institute of Solid Mechanics (ISM), Department of Engineering Mechanics, Tsinghua University, and Bachelor’s degree from Civil Aviation University of China in Tianjin. I also worked as a research assistant at Microsoft Research AI4Science (2022-2023). I will come to Bauhaus University as a guest researcher in Weimar supervised by Prof. Rabczuk for AI for mechanics (2023-2024).
I am majoring in solid mechanics and have rich experience in physical theory, computation, and experiment. I mainly focus on solving Partial Differential Equations of solid mechanics based on the physics-informed neural networks(PINNs), operator learning and deep energy method. My research interest is AI4Science, specifically AI for mechanics.
🔥 News
- 2024.06: 🎉🎉 I finish KINN and submit to CMAME!
- 2024.06: 🎉🎉 I get the recommendation letter from big name Timon Rabczuk!
- 2024.06: 🎉🎉 The paper with my friend Jinshuai Bai is accepted by CMAME.
- 2024.05: 🎉🎉 I finish the review paper (AI4PDEs in solid mechanics) and submit to a Chinese Journal!
- 2024.05: 🎉🎉 I go to the Xiaoying Zhuang group, and have a presentation!
- 2024.04: 🎉🎉 I go to German, and join the Timon Rabczuk group!
- 2024.03: 🎉🎉 I attend Conference of Chinese Solid Mechanics, and served as a host!
- 2024.02: 🎉🎉 I Complete the paper using FNO for homogenization and submit to Nature Machine Intelligence!
- 2024.01: 🎉🎉 I coorperate with my friend Dr. Liu for using FNO in multi-scale of RVE.
- 2023.11: 🎉🎉 Coorperate with my friend Jinshuai Bai for his paper about some numericial phenomonons in Deep Energy Methods. Let’s keep us fingers crossed.
- 2023.11: 🎉🎉 the combination between RVE and PINNs is accepted by Renewable Energy
- 2023.09: 🎉🎉 Past PH.D. program in Tsinghua University! I will come to be a first-year Ph.D in Stepember, 2024.
- 2023.08: 🎉🎉 Complete my second paper and submit to CMAME!
- 2023.08: 🎉🎉 I have an oral presentation at CCCM2023 conference!
- 2023.07: 🎉🎉 I coorperate with my friend Dr. Liu for the combination between RVE and PINNs.
- 2023.07: 🎉🎉 I participate in DACOMA-23 conference and meet with Prof. Rabczuk!
- 2023.05: 🎉🎉 I participate in DDCM2023 and have an oral presentation!
- 2023.03: 🎉🎉 BINN is accepted by CMAME!
- 2022.11: 🎉🎉 I join Microsoft Research as a Research Assistant for AI4Science!
- 2022.09: 🎉🎉 I participate in (DACOMA-22) and win the best paper award!
- 2022.08: 🎉🎉 CENN is accepted by CMAME!
- 2022.06: 🎉🎉 I receive my master degree from Tsinghua University!
📝 Publications
Yizheng Wang, Jia Sun, Wei Li, Zaiyuan Lu, Yinghua Liu
- A deep energy method with subdomains, suitable to solve non-uniform problems with complex boundaries.
Jia Sun, Yinghua Liu, Yizheng Wang, Zhenhan Yao, and Xiaoping Zheng
- Combine boundary element method with PINNs for the first time.
Bokai Liu, Yizheng Wang, Timon Rabczuk, Thomas Olofsson, Weizhuo Lu
- We propose a hierarchical multi-scale model RVE utilizing Physics-Informed Neural Networks (PINNs).
Jinshuai Bai, Gui-Rong Liu, Timon Rabczuk, Yizheng Wang, Xi-Qiao Feng, YuanTong Gu
- We proposed a robust radial point interpolation method empowered with neural network solvers (RPIM-NNS) for solving highly nonlinear solid mechanics problems.
📝 Under Review
Yizheng Wang, Jia Sun, Jinshuai Bai, Cosmin Anitescu, Mohammad Sadegh Eshaghi, Xiaoying Zhuang, Timon Rabczuk, and Yinghua Liu
- We propose different PDE forms based on KAN instead of MLP, termed Kolmogorov-Arnold-Informed Neural Network (KINN). We systematically compare MLP and KAN in various numerical examples of PDEs, including multi-scale, singularity, stress concentration, nonlinear hyperelasticity, heterogeneous, and complex geometry problems. Our results demonstrate that KINN significantly outperforms MLP in terms of accuracy and convergence speed for numerous PDEs in computational solid mechanics, except for the complex geometry problem. This highlights KINN’s potential for more efficient and accurate PDE solutions in AI for PDEs.
A deep complementary energy method for solid mechanics using minimum complementary energy principle
Yizheng Wang, Jia Sun, Timon Rabczuk, and Yinghua Liu
- We propose the deep complementary energy method (DCEM) based on the principle of minimum complementary energy for the first time. DCEM outperforms DEM in terms of stress accuracy and efficiency and has an advantage in dealing with complex displacement boundary conditions. A deep complementary energy operator method (DCEM-O) by combining operator learning with physical equations is proposed.
Yizheng Wang, Xiang Li, Ziming Yan, Jinshuai Bai, Bokai Liu, Timon Rabczuk, and Yinghua Liu
- We propose a numerical homogenization model based on operator learning: HomoGenius. The proposed model can quickly provide homogenization results for arbitrary geometries, materials, and resolutions, increasing the efficiency by a factor of 80 compared to traditional numerical homogenization methods.
Bokai Liu, Yizheng Wang, Yinghua Liu, Kailun Feng, Timon Rabczuk, and Thomas Olofsson
- <>: These authors contributed equally to this work. We propose a hierarchical multi-scale model utilizing Deep learning-based Neural Operators. This model enables precise prediction and analysis of the material’s thermal conductivity at both the fine-scale and coarse-scale, from nano to macro scales.
🎖 Honors and Awards
- 2022.09 I participate in (DACOMA-22) and win the best paper award!
- 2022.01 My team placed 18th out of 3537 contestants in the 2021 Kaggle Competition Public Committee (I was the first author)!
- 2020.06 My team won the 12th place in the 2020 Baidu Star Developer Competition (out of 2,312 teams)!
- 2019.09 - 2022.06 I participated in the John Ma Cup of Tsinghua University in total 6 times, and won 5 times in the top 8 of the school!
📖 Educations
- 2024.08 - future, Ph.D in Department of Engineering Mechanics, Tsinghua University, Beijing, China
- 2019.08 - 2022.06, Master in Department of Engineering Mechanics, Tsinghua University, Beijing, China
- 2012.08 - 2016.06, Bachelor from College of Air Traffic Management, Civil Aviation University of China, Tianjin, China
💬 Conference
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2024.04, CCSM2024, Nanjing, China: Yizheng Wang, and Yinghua Liu. “AI for PDEs in solid mechanics”. (Oral presentation and host in computaiontal solid mechanics)
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2023.08, CCCM2023, Dalian, China: Yizheng Wang, and Yinghua Liu. “Deep energy method based on the principle of possible work” Oral Presentation at “Artificial Intelligence and Its Applications in Computational Mechanics”. (Oral presentation)
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2023.07, DACOMA-23, Beijing, China: only participate
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2023.05, DDCM2023, Dalian, China: Yizheng Wang, and Yinghua Liu. “A deep complementary energy method for solid mechanics using minimum complementary energy principle.” (Oral presentation)
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2022.09, DACOMA-22, Beijing, China: Yizheng Wang. “Solving Partial Differential Equations of Solid Mechanics Based on PINN.” Won “Best Paper Awards” (Master’s Thesis Supervised by Prof. Yinghua Liu) (Oral presentation)
- 2022.07, WCCM2022, Yokohama, Japan: Yizheng Wang, and Yinghua Liu. “A Physics-informed Complementary Energy Form in Solid Mechanics.” Presented at Minisymposium MS1716 “Data-driven Approaches in Computational Solid Mechanics.” (Oral presentation)
💻 PROFESSIONAL EXPERIENCE
- 2022.11 - 2023.07, Research Assistant at Microsoft Research AI4Science, Beijing, China.
- 2020.06 - 2020.08, Research Assistant at Shanxi Intelligence Institute of Big Data Technology and Innovation (SIBD) , Taiyuan, China.
- 2016.07 - 2018.05, Flight Dispatcher at Zhejiang Loong Airlines Co., Ltd., Xiaoshan, China.