Project 162: Machine Learning-Assisted Physical Field Reconstruction

Contact Information:

Assoc. Prof. Helin Gong      

Email: gonghelin@sjtu.edu.cn

Project Description and Objectives:

This project aims to develop a machine learning-assisted framework for reconstructing physical fields in engineering contexts, such as temperature distribution in nuclear reactors or stress fields in structural engineering. The objective is to leverage machine learning algorithms to enhance the accuracy and efficiency of physical field reconstruction. This involves data preprocessing, model selection, training, and evaluation, with a focus on understanding the underlying physics and engineering principles. The project seeks to bridge the gap between traditional engineering methods and contemporary AI techniques, offering innovative solutions to complex engineering problems.

Eligibility Requirements:

Undergraduate or graduate students in Engineering, Computer Science, or related fields.

Basic knowledge of machine learning and data processing.

Familiarity with engineering concepts related to physical field analysis.

Proficiency in programming languages such as Python.

Main Tasks:

Data collection and preprocessing for physical field analysis.

Selection and training of appropriate machine learning models.

Evaluation of model performance and analysis of results.

Documentation and presentation of findings.

Website: 

Lab: N/A

School: https://speit.sjtu.edu.cn/