5 citations · 9 across the 4 of their papers we have counts for
5 papers
Designing Neural Networks for Hyperbolic Conservation Laws
Zhen Chen, Anne Gelb, Yoonsang Lee
We propose a new data-driven method to learn the dynamics of an unknown hyperbolic system of conservation laws using deep neural networks. Inspired by classical methods in numerica…
Hierarchical Learning to Solve Partial Differential Equations Using Physics-Informed Neural Networks
Jihun Han, Yoonsang Lee
The neural network-based approach to solving partial differential equations has attracted considerable attention due to its simplicity and flexibility in representing the solution…
Sampling error correction in ensemble Kalman inversion
Yoonsang Lee
Ensemble Kalman inversion is a parallelizable derivative-free method to solve inverse problems. The method uses an ensemble that follows the Kalman update formula iteratively to so…
regularization for ensemble Kalman inversion
Yoonsang Lee
Ensemble Kalman inversion (EKI) is a derivative-free optimization method that lies between the deterministic and the probabilistic approaches for inverse problems. EKI iterates the…
Understanding the Stability of Deep Control Policies for Biped Locomotion
Hwangpil Park, Ri Yu, Yoonsang Lee +2
Achieving stability and robustness is the primary goal of biped locomotion control. Recently, deep reinforce learning (DRL) has attracted great attention as a general methodology f…