11 citations · 36 across the 12 of their papers we have counts for
14 papers
Learning Human-like Locomotion Based on Biological Actuation and Rewards
Minkwan Kim, Yoonsang Lee
We propose a method of learning a policy for human-like locomotion via deep reinforcement learning based on a human anatomical model, muscle actuation, and biologically inspired re…
Performance-Based Biped Control using a Consumer Depth Camera
Yoonsang Lee, Taesoo Kwon
We present a technique for controlling physically simulated characters using user inputs from an off-the-shelf depth camera. Our controller takes a real-time stream of user poses a…
Adaptive Tracking of a Single-Rigid-Body Character in Various Environments
Taesoo Kwon, Taehong Gu, Jaewon Ahn +1
Since the introduction of DeepMimic [Peng et al. 2018], subsequent research has focused on expanding the repertoire of simulated motions across various scenarios. In this study, we…
Weighted inhomogeneous regularization for inverse problems with indirect and incomplete measurement data
Bosu Choi, Jihun Han, Yoonsang Lee
Regularization is a critical technique for ensuring well-posedness in solving inverse problems with incomplete measurement data. Traditionally, the regularization term is designed…
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…
A Neural Network Approach for Homogenization of Multiscale Problems
Jihun Han, Yoonsang Lee
We propose a neural network-based approach to the homogenization of multiscale problems. The proposed method uses a derivative-free formulation of a training loss, which incorporat…
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…
Inhomogenous Regularization with Limited and Indirect Data
Jihun Han, Yoonsang Lee
For an ill-posed inverse problem, particularly with incomplete and limited measurement data, regularization is an essential tool for stabilizing the inverse problem. Among various…
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…
Fast integrators for dynamical systems with several temporal scales
Yoonsang Lee, Bjorn Engquist
We propose a fast integrator to a class of dynamical systems with several temporal scales. The proposed method is developed as an extension of the variable step size Heterogeneous…
Multiscale numerical methods for passive advection-diffusion in incompressible turbulent flow fields
Yoonsang Lee, Bjorn Engquist
We propose a seamless multiscale method which approximates the macroscopic behavior of the passive advection-diffusion equations with steady incompressible velocity fields with mul…
Variable Step Size Multiscale Methods for Stiff and Highly Oscillatory Dynamical Systems
Yoonsang Lee, Bjorn Engquist
We present a new numerical multiscale integrator for stiff and highly oscillatory dynamical systems. The new algorithm can be seen as an improved version of the seamless Heterogene…