Pseudo-Differential Neural Operator: Generalized Fourier Neural Operator for Learning Solution Operators of Partial Differential Equations

1 code implementation • 28 Jan 2022 • Jin Young Shin, Jae Yong Lee, Hyung Ju Hwang

We combine the PDIO with the neural operator to develop a \textit{pseudo-differential neural operator} (PDNO) and learn the nonlinear solution operator of PDEs.

Solving PDE-constrained Control Problems Using Operator Learning

no code implementations • 9 Nov 2021 • Rakhoon Hwang, Jae Yong Lee, Jin Young Shin, Hyung Ju Hwang

Once the surrogate model is trained in Phase 1, the optimal control can be inferred in Phase 2 without intensive computations.

Operator learning

Prior Preference Learning from Experts:Designing a Reward with Active Inference

no code implementations • 22 Jan 2021 • Jin Young Shin, Cheolhyeong Kim, Hyung Ju Hwang

In this paper, we claim that active inference can be interpreted using reinforcement learning (RL) algorithms and find a theoretical connection between them.

Reinforcement Learning (RL)

Prior Preference Learning From Experts: Designing A Reward with Active Inference

no code implementations • 1 Jan 2021 • Jin Young Shin, Cheolhyeong Kim, Hyung Ju Hwang

In this paper, we claim that active inference can be interpreted using reinforcement learning (RL) algorithms and find a theoretical connection between them.

Reinforcement Learning (RL)