Machine Learning and New Framework for Solving Partial Differential Equations
|
Organizer(s): |
Name:
|
Affiliation:
|
Country:
|
Jingrun Chen
|
Suzhou Institute for Advanced Research, University of Science and Technology of China
|
Peoples Rep of China
|
Haijun Yu
|
Academy of Mathematics and Systems Science, Chinese Academy of Sciences
|
Peoples Rep of China
|
Shuo Zhang
|
Academy of Mathematics and Systems Science, Chinese Academy of Sciences
|
Peoples Rep of China
|
|
|
|
|
|
|
|
|
Introduction:
| | Recently, it has been a groundbreaking trend that synergistically combines the principles of partial differential equation (PDE) solvers and machine learning (ML) techniques to advance the solution of complex PDE systems. The new methodology may transcend traditional approaches in harnessing the predictive power of ML algorithms while preserving the mathematical rigor and interpretability inherent in PDE-based models. The interaction between PDE and ML may also introduce comprehensively new insights to the traditional approaches of PDE solution as well as the ML techniques.
This mini-symposium proposes to gather experts in the fields of PDE solution and machine learning to communicated latest progress in the new framework of solving PDE particularly by/for machine learning. The framework is demonstrated through a series of case studies spanning diverse application domains. Moreover, the frameworkâs modular design allows for seamless integration into existing computational workflows and mathematical rigors, fostering broader adoption across scientific and engineering disciplines, and unlocking novel avenues for interdisciplinary research.
|
|
|
|
| |
