Phymaths podcast # 47 || Guest: Dr. Harold Erbin

Dr. Harold Erbin is a theoretical physicist who works on string field theory and string compactifications. He uses AI to solve problems in theoretical physics and is the author of the most recent book on string field theory. A list of his works can be found below. Some links 1) Dr. Erbin's works: https://inspirehep.net/authors/1273902?ui-citation-summary=true 2) Support this channel: https://www.patreon.com/phymaths 3) Become a YouTube member: https://www.youtube.com/channel/UC3nAENP4F5GKrGk9BLNFLvw/join 4) Link to the high energy physics resources page on my website: https://hassaansaleem.com/graduate-level-theoretical-physics-resources-in-progress/ Contact Info My website: hassaansaleem.com Follow on Instagram: https://www.instagram.com/hassaan.3142/ Join the Whatsapp channel: https://whatsapp.com/channel/0029Va9DkjmAzNbs7IlGuv1X Follow on Twitter: https://twitter.com/Hassaan_PHY Email Queries: [email protected] Time stamps (0:00) Coming Up (0:25) Introduction of the guest (0:46) What is String Field Theory (SFT)? Why use it? (4:05) Functional fields in SFT (6:24) Mathematical Techniques for SFT (9:27) BRST and BV action (13:12) Background independence of string theory (29:39) Using pure spinor formalism (36:10) Using machine learning for SFT (50:38) Geometric BV algebras (57:03) Training the neural network (1:14:31) Constraints on closed SFT vertices (1:18:43) QFT - Neural Network correspondence (1:28:22) UV limit for Neural Network? (1:31:37) Experience with Ashoke Sen (1:44:40) His book on SFT (1:58:22) (People's question) Is string theory the end of Physics? (2:01:14) (People's question) Learning complex manifolds (2:03:31) (People's question) Learning machine learning early