FSI Overview & Intro to Machine Learning for CAE: Dr. Chennakesava Kadapa, Episode 9
Published 2024-05-03
🌎 Chenna’s Website: www.napier.ac.uk/people/chennakesava-kadapa?tab=2
🔗 Kade’s LinkedIn Profile: www.linkedin.com/in/kade-j-beck/
Chenna’s FSI GitHub repo:github.com/chennachaos/awesome-FSI
Chenna’s ML Paper: arxiv.org/abs/2112.12054
Chenna’s courses with Flowthermolab: www.flowthermolab.com/all-courses/
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TIME STAMPS
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0:00 : Sneak Preview of Episode
0:24 : Introducing Chenna
0:52 : Chenna’s background
1:54 : First experience with simulation
4:38 : New FEA modeling practice
7:32 : Why Chenna did a PhD
9:50 : FSI for beginners
10:58 : Resources to get started with FSI
14:13 : Challenges for FSI in industry
19:36 : Bottlenecks for FSI
22:10 : FSI challenges in academia
23:43 : Chenna’s teaching approach
27:04 : Chenna’s courses with Flowthermolab
29:06 : Critical questions for Machine Learning
38:55 : ML Fundamentals
40:57 : Where ML can provide the most value
45:21 : CG and DG being used more in industry?
50:45 : why mesh generation limits DG in industry
57:31 : GPUs vs CPUs
1:01:58 : On premise vs Cloud HPC
1:04:15 : Dealing with information overload
1:07:56 : Patience for CFD
1:10:52 : Advice Chenna would give to undergrad self
Podcast Recorded: April 4th, 2024; Subscriber release count: 933
Dr. Chennakesava Kadapa is a Lecturer in Mechanical Engineering at Edinburgh Napier University. His research focuses on cutting-edge simulation techniques for multi-physics problems in solid mechanics, fluid mechanics, fluid structure interaction, and electro-magneto-mechanics. He received a PhD in Mechanical Engineering from Swansea University and received the prestigious Zienkiewicz Scholars
All Comments (3)
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basis of science was verification by independent observers. improved communication reduces independence, which reduces posssibility of scientific verification. mankind might find its research capability reducing bdcause of the ease of communication.
