71 citations · 77 across the 12 of their papers we have counts for
6 papers · 1 filter
Offline Reinforcement Learning for Fluid Controls: Data-based Multi-observational Policy Extraction
Deepak Akhare, Luning Sun, Xin-Yang Liu +4
Active flow control is a fundamental application in engineering. Recent advances in deep reinforcement learning have made progress in this field. However, the classical online RL a…
Multi-fidelity Reinforcement Learning Control for Complex Dynamical Systems
Luning Sun, Xin-Yang Liu, Siyan Zhao +3
Controlling instabilities in complex dynamical systems is challenging in scientific and engineering applications. Deep reinforcement learning (DRL) has seen promising results for a…
A Pre-trained Data Deduplication Model based on Active Learning
Haochen Shi, Xinyao Liu, Fengmao Lv +4
In the era of big data, the issue of data quality has become increasingly prominent. One of the main challenges is the problem of duplicate data, which can arise from repeated entr…
A Missing Value Filling Model Based on Feature Fusion Enhanced Autoencoder
Xinyao Liu, Shengdong Du, Tianrui Li +2
With the advent of the big data era, the data quality problem is becoming more critical. Among many factors, data with missing values is one primary issue, and thus developing effe…
Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics
Xin-Yang Liu, Min Zhu, Lu Lu +2
Traditional data-driven deep learning models often struggle with high training costs, error accumulation, and poor generalizability in complex physical processes. Physics-informed…
Physics-informed Dyna-Style Model-Based Deep Reinforcement Learning for Dynamic Control
Xin-Yang Liu, Jian-Xun Wang
Model-based reinforcement learning (MBRL) is believed to have much higher sample efficiency compared to model-free algorithms by learning a predictive model of the environment. How…