Neural Canonical Transformations


Wang, L. (2023). Neural Canonical Transformations. Perimeter Institute. https://pirsa.org/23010099


Wang, Lei. Neural Canonical Transformations. Perimeter Institute, Jan. 27, 2023, https://pirsa.org/23010099


          @misc{ pirsa_PIRSA:23010099,
            doi = {10.48660/23010099},
            url = {https://pirsa.org/23010099},
            author = {Wang, Lei},
            keywords = {Other},
            language = {en},
            title = {Neural Canonical Transformations},
            publisher = {Perimeter Institute},
            year = {2023},
            month = {jan},
            note = {PIRSA:23010099 see, \url{https://pirsa.org}}

Lei Wang Chinese Academy of Sciences

Talk number PIRSA:23010099
Talk Type Scientific Series


Canonical transformations play fundamental roles in simplifying and solving physical systems. However, their design and implementation can be challenging in the many-particle setting. Viewing canonical transformations from the angle of learnable diffeomorphism reveals a fruitful connection to normalizing flows in machine learning. The key issue is then how to impose physical constraints such as symplecticity, unitarity, and permutation equivariance in the flow transformations. In this talk, I will present the design and application of neural canonical transformations for several physical problems. Symplectic flow identifies independent and nonlinear modes of classical Hamiltonians and natural datasets. Fermi flow variationally solves ab initio many-electron problems at finite temperatures.
[1] Shuo-Hui Li, Chen-Xiao Dong, Linfeng Zhang, and Lei Wang, Phys. Rev. X 10, 021020 (2020)
[2]  Hao Xie, Linfeng Zhang, and Lei Wang, J. Mach. Learn. , 1, 38 (2022)

Zoom link:  https://pitp.zoom.us/j/98830940500?pwd=WjdydGY5aS9QQzk5SnI0TE1xMkwrdz09