Engineering Basics
This course is intended to help the attendees to have a quick grasp of engineering basics, a quick entry to future engineering related research projects, and a quick mastery of communication language with professional engineers. The course condenses 16 courses that normally take 20 semesters to teach in one semester.
Lecturer
Date
23rd October ~ 15th December, 2025
Location
| Weekday | Time | Venue | Online | ID | Password |
|---|---|---|---|---|---|
| Monday,Thursday | 19:20 - 21:45 | A3-1-301 | ZOOM 01 | 928 682 9093 | BIMSA |
Prerequisite
Basic knowledge on calculus, linear algebra, differential equations，and general physics.
Syllabus
（1）Solid Mechanics
a) Theoretical Mechanics
b) Material Mechanics
c) Elastic Mechanics
（2）Fluid Mechanics
a) Basics of Fluid Dynamics
b) Gas Dynamics
c) Viscous Fluid Mechanics
d) Turbulence
（3）Thermal Engineering
a) Thermodynamics
b) Heat Transfer
c) Mass Transfer
（5）Dynamics with Chemical Reactions
a) Chemical Kinetics
b) Combustion
c) Galaxy Structure and Gathering of Myxomycete Amoeba
d) Microbiological Systems
（6）Extended Topics
a) Percolation Mechanics
b) Physics Informed Neural Networks
a) Theoretical Mechanics
b) Material Mechanics
c) Elastic Mechanics
（2）Fluid Mechanics
a) Basics of Fluid Dynamics
b) Gas Dynamics
c) Viscous Fluid Mechanics
d) Turbulence
（3）Thermal Engineering
a) Thermodynamics
b) Heat Transfer
c) Mass Transfer
（5）Dynamics with Chemical Reactions
a) Chemical Kinetics
b) Combustion
c) Galaxy Structure and Gathering of Myxomycete Amoeba
d) Microbiological Systems
（6）Extended Topics
a) Percolation Mechanics
b) Physics Informed Neural Networks
Audience
Advanced Undergraduate
, Graduate
, Postdoc
, Researcher
Video Public
Yes
Notes Public
Yes
Language
Chinese
Lecturer Intro
Dr. Zhang received his bachelor's, master's, and doctor's degrees from Zhejiang University, Peking University, and Massachusetts Institute of Technology. He is currently a professor at the Beijing Institute of Mathematical Sciences and Applications, in the artificial intelligence and machine learning research group. He is currently interested in developing machine learning algorithms driven by both data and existing domain knowledge, and applying them to the interpretation and quantification of various physical, biological and social phenomena.