no code implementations • 22 Nov 2022 • Jaehoon Lee, Chan Kim, Gyumin Lee, Haksoo Lim, Jeongwhan Choi, Kookjin Lee, Dongeun Lee, Sanghyun Hong, Noseong Park
Forecasting future outcomes from recent time series data is not easy, especially when the future data are different from the past (i. e. time series are under temporal drifts).
2 code implementations • 11 Nov 2021 • Jeehyun Hwang, Jeongwhan Choi, Hwangyong Choi, Kookjin Lee, Dongeun Lee, Noseong Park
On the other hand, neural ordinary differential equations (NODEs) are to learn a latent governing equation of ODE from data.
no code implementations • 29 Sep 2021 • Jungeun Kim, Seunghyun Hwang, Jeehyun Hwang, Kookjin Lee, Dongeun Lee, Noseong Park
In other words, the knowledge contained by the learned governing equation can be injected into the neural network which approximates the PDE solution function.
no code implementations • 1 Jan 2021 • Jungeun Kim, Seunghyun Hwang, Jihyun Hwang, Kookjin Lee, Dongeun Lee, Noseong Park
Neural ordinary differential equations (neural ODEs) introduced an approach to approximate a neural network as a system of ODEs after considering its layer as a continuous variable and discretizing its hidden dimension.
1 code implementation • 4 Dec 2020 • Jungeun Kim, Kookjin Lee, Dongeun Lee, Sheo Yon Jin, Noseong Park
We present a method for learning dynamics of complex physical processes described by time-dependent nonlinear partial differential equations (PDEs).
no code implementations • 11 Jun 2019 • Duanshun Li, Jing Liu, Noseong Park, Dongeun Lee, Giridhar Ramachandran, Ali Seyedmazloom, Kookjin Lee, Chen Feng, Vadim Sokolov, Rajesh Ganesan
0-1 knapsack is of fundamental importance in computer science, business, operations research, etc.