rmojgani/LPINNs
To address some of the failure modes in training of physics informed neural networks, a Lagrangian architecture is designed to conform to the direction of travel of information in convection-diffusion equations, i.e., method of characteristic; The repository includes a pytorch implementation of PINN and proposed LPINN with periodic boundary conditions
Python
Stargazers
- ahmadijo
- anonymousr007@hilbertquantum @QuTLab
- bbw7561135Yunnan University
- BraveDrXuTF
- bryantxy
- ce107
- devidaskgodse
- Dingugu
- GlobalStudents
- haolpkuBeijing, China
- hxm2023
- iiStaSiiEclipseSpaceworsSystems
- jakharkaranUS
- jamieborder
- jayfgraham
- jiaoly
- jinge0520
- julian-py-creator
- lyushiyuTechnical University of Munich
- ManuelPetersmannhttps://www.k-ai.at/
- Mehdishishehbor
- niuffs
- rabiumusahUniversity for Development Studies
- RahulSundarBio-mimetics Lab, Indian Institute of Technology Madras
- RancyChepchirchirUniversity of Hull
- reyhashemi
- rmojgani
- Shen-Yuying
- TengoNguyen
- vjirgale
- Wang-Chbo
- xgxg1314
- xuanxuan0923Beijing
- xubonan
- zhongly1021Ann Arbor
- Zhuomin-ZhouMonash University