1 citations · 2 across the 6 of their papers we have counts for
6 papers
LinguaFluid: Language Guided Fluid Control via Semantic Rewards in Reinforcement Learning
Aoming Liang, Chi Cheng, Dashuai Chen +2
In the domain of scientific machine learning, designing effective reward functions remains a challenge in reinforcement learning (RL), particularly in environments where task goals…
LE-PDE++: Mamba for accelerating PDEs Simulations
Aoming Liang, Zhaoyang Mu, Qi liu +3
Partial Differential Equations are foundational in modeling science and natural systems such as fluid dynamics and weather forecasting. The Latent Evolution of PDEs method is desig…
DamFormer: Generalizing Morphologies in Dam Break Simulations Using Transformer Model
Zhaoyang Mul, Aoming Liang, Mingming Ge +3
The interaction of waves with structural barriers such as dams breaking plays a critical role in flood defense and tsunami disasters. In this work, we explore the dynamic changes i…
MM: Learning controllable Multi of experts and multi-scale operators are the Partial Differential Equations need
Aoming Liang, Zhaoyang Mu, Pengxiao Lin +5
Learning the evolutionary dynamics of Partial Differential Equations (PDEs) is critical in understanding dynamic systems, yet current methods insufficiently learn their representat…
Learning Adaptive Hydrodynamic Models Using Neural ODEs in Complex Conditions
Cong Wang, Aoming Liang, Fei Han +4
Reinforcement learning-based quadruped robots excel across various terrains but still lack the ability to swim in water due to the complex underwater environment. This paper presen…
ModCube: Modular, Self-Assembling Cubic Underwater Robot
Jiaxi Zheng, Guangmin Dai, Botao He +5
This paper presents a low-cost, centralized modular underwater robot platform, ModCube, which can be used to study swarm coordination for a wide range of tasks in underwater enviro…