I'm so glad to see the video about my favorite paper. Actually, I was highly motivated by your paper with Eivazi, and therefore conducted the research extending your beta-VAE framework to reduced order modeling (ROM). Though our paper handles only a simple benchmark flowfield compared to your paper, we hope that our work can be interesting to those impressed with this video. p.s. I am also following your work on physics-informed neural networks and looking forward to your next interesting research topic (I am a big fan of your research group in KTH)!
For those who are interested in our work: Yu-Eop Kang, Sunwoong Yang, and Kwanjung Yee , "Physics-aware reduced-order modeling of transonic flow via β-variational autoencoder", Physics of Fluids 34, 076103 (2022)
The way you have explained the cfd quite interesting...Thankyou
I'm so glad to see the video about my favorite paper. Actually, I was highly motivated by your paper with Eivazi, and therefore conducted the research extending your beta-VAE framework to reduced order modeling (ROM). Though our paper handles only a simple benchmark flowfield compared to your paper, we hope that our work can be interesting to those impressed with this video. p.s. I am also following your work on physics-informed neural networks and looking forward to your next interesting research topic (I am a big fan of your research group in KTH)!
For those who are interested in our work: Yu-Eop Kang, Sunwoong Yang, and Kwanjung Yee , "Physics-aware reduced-order modeling of transonic flow via β-variational autoencoder", Physics of Fluids 34, 076103 (2022)