Explores geometric quantization, the process of quantizing classical systems to obtain quantum systems using mathematical techniques like pseudo-differential calculus.
Explores enhancing machine learning predictions by refining error metrics and applying constraints for improved accuracy in electron density predictions.
Covers the Calculus of Variations to find ground states in quantum mechanics by minimizing energy, discussing the Euler Lagrange equation and the Fundamental Theorem of Young Measure Theory.
