27 citations · 55 across the 11 of their papers we have counts for
14 papers
Efficient On-policy Visual-RL via Stochastic Decoupled Policy Gradient
Haoxiang You, Yilang Liu, Davis Zong +5
We present the stochastic decoupled policy gradient (SDPG), a lightweight visual reinforcement learning (RL) method that trains diverse visuomotor control policies end-to-end withi…
Asymptotically Optimal Ergodic Coverage on Generalized Motion Fields
Christian Hughes, Yilang Liu, Yanis Lahrach +6
Autonomous robotic exploration in remote and extreme environments allows scientists to model complex transport phenomena and collective behaviors described by continuously deformin…
Sample-Based Hybrid Mode Control: Asymptotically Optimal Switching of Algorithmic and Non-Differentiable Control Modes
Yilang Liu, Haoxiang You, Ian Abraham
This paper investigates a sample-based solution to the hybrid mode control problem across non-differentiable and algorithmic hybrid modes. Our approach reasons about a set of hybri…
A novel parallelizable convergence accelerating method: Pointwise Frequency Damping
Zikun Liu, Xukun Wang, Yilang Liu +1
This paper proposes a novel class of data-driven acceleration methods for steady-state flow field solvers. The core innovation lies in predicting and assigning the asymptotic limit…
Accelerating Visual-Policy Learning through Parallel Differentiable Simulation
Haoxiang You, Yilang Liu, Ian Abraham
In this work, we propose a computationally efficient algorithm for visual policy learning that leverages differentiable simulation and first-order analytical policy gradients. Our…
A data-driven convergence booster for accelerating and stabilizing pseudo time-stepping
Xukun Wang, Yilang Liu, Xiang Yang +1
This paper introduces a novel data-driven convergence booster that not only accelerates convergence but also stabilizes solutions in cases where obtaining a steady-state solution i…
Towards a Generalized SA Model: Symbolic Regression-Based Correction for Separated Flows
Xuxiang Sun, Xianglin Shan, Yilang Liu +1
This study focuses on the numerical simulation of high Reynolds number separated flows and proposes a data-driven approach to improve the predictive capability of the SA turbulence…
A novel convergence enhancement method based on Online Dimension Reduction Optimization
Wenbo Cao, Yilang Liu, Xianglin Shan +2
Iterative steady-state solvers are widely used in computational fluid dynamics. Unfortunately, it is difficult to obtain steady-state solution for unstable problem caused by physic…
High Reynolds number airfoil turbulence modeling method based on machine learning technique
Xuxiang Sun, Wenbo Cao, Yilang Liu +2
In this paper, a turbulence model based on deep neural network is developed for turbulent flow around airfoil at high Reynolds numbers. According to the data got from the Spalart-A…
Analysis on numerical stability and convergence of RANS turbulence models from the perspective of coupling modes
Yilang Liu, Weiwei Zhang, Zhenhua Xia
Reynolds-averaged Navier-Stokes simulations are still the main method to study complex flows in engineering. However, traditional turbulence models cannot accurately predict flow f…
An Energy-Saving Snake Locomotion Gait Policy Obtained Using Deep Reinforcement Learning
Yilang Liu, Amir Barati Farimani
Snake robots, comprised of sequentially connected joint actuators, have recently gained increasing attention in the industrial field, like life detection in narrow space. Such robo…
A new data assimilation method of recovering turbulent flow field at high-Reynolds numbers for turbulence machine learning
Yilang Liu, Weiwei Zhang
This paper proposes a new data assimilation method for recovering high fidelity turbulent flow field around airfoil at high Reynolds numbers based on experimental data, which is ca…
Machine learning methods for turbulence modeling in subsonic flows over airfoils
Weiwei Zhang, Linyang Zhu, Yilang Liu +1
Reynolds-Averaged Navier-Stokes(RANS) method will still play a vital role in the following several decade in aerospace engineering. Although RANS models are widely used, empiricism…
Mode Multigrid - A novel convergence acceleration method
Yilang Liu, Weiwei Zhang, Jiaqing Kou
This paper proposes a mode multigrid (MMG) method, and applies it to accelerate the convergence of the steady state flow on unstructured grids. The dynamic mode decomposition (DMD)…