Partial differential equations (PDEs) play a pivotal role in a wide range of scientific and engineering domains, including fluid mechanics, quantum mechanics, and financial engineering. However, solving high-dimensional PDEs has always been a formidable challenge due to “curse of dimensionality”. In recent years, deep learning has emerged as a transformative technology, offering innovative approaches to tackle PDEs, particularly those with high dimensions. By leveraging the power of deep learning, we can revolutionize the field and develop novel methodologies for solving complex PDEs.
The main objective of this workshop is to provide a platform for renowned scholars from around the world to come together and share their experiences and insights in the realm of deep learning and numerical methods for PDEs. Through the exchange of innovative ideas and collaborative research, we aim to pave the way for future research directions and advancements in this field. During the workshop, we will showcase the latest advancements in the application of deep learning and numerical methods to PDEs, with a specific emphasis on their applications in science and engineering. By fostering interdisciplinary collaboration, we seek to explore faster, more effective, and highly accurate solution algorithms that can address the complex practical issues associated with PDEs. We extend a warm invitation to researchers, experts, and practitioners who are actively involved or interested in this field to join us at the workshop. Let us come together and collectively push the boundaries of knowledge, foster collaboration, and shape the future of solving PDEs using deep learning and numerical methods.
1. Conference Dates: June 21-23, 2024 (Talks scheduled for June 22-23)
2. Conference Venue: Meeting Room 2-1, Math Building, Xi'an Jiaotong University (Xingqing Campus)
3. Invited Speakers (in alphabetical order by last name)
Benedikt Brantner Max-Planck-Institut für Plasmaphysik
Mingchao Cai Morgan State University
Long Chen University of California, Irvine
Suchuan Dong Purdue University
Xiaobing Feng University of Tennessee
Ruchi Guo Chinese University of Hong Kong
Alexander Heinlein Delft University of Technology
Jianguo Huang Shanghai Jiao Tong University
Jiwei Jia Jilin University
Lili Ju University of South Carolina
Young Ju Lee Texas State University
Dong Liu Nuclear Power Institute of China
Xinliang Liu King Abdullah University of Science and Technology
Pingbing Ming Chinese Academy of Sciences
Pengtao Sun University of Nevada, Las Vegas
Fei Wang Xi’an Jiaotong University
Zhiguo Wang Xi’an Jiaotong University
Weiwei Zhang Northwestern Polytechnical University
4. Sponsor: Tianyuan Mathematical Center in Northwest China
5. Organization Committee (in alphabetical order by last name)
Long Chen University of California, Irvine
Zhiguo Wang Xi’an Jiaotong University
Fei Wang Xi’an Jiaotong University
6. Contact Information
Fei Wang, Email: feiwang.xjtu@xjtu.edu.cn
