71 citations · 77 across the 12 of their papers we have counts for
12 papers
Reinforcement-learning control of turbulence transition in the modified Hasegawa-Wakatani system
Luning Sun, Ben Zhu, Xin-Yang Liu +2
Control of plasma turbulence remains a long-standing challenge in magnetically confined fusion research. Here, we explore a reinforcement-learning (RL) approach for bidirectional c…
A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction
Xin-Yang Liu, Xiantao Fan, Jian-Xun Wang
Predicting complex spatiotemporal dynamics in physical processes often demands computationally expensive numerical methods or data-driven neural networks that suffer from high trai…
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…
Generative Latent Diffusion Model for Inverse Modeling and Uncertainty Analysis in Geological Carbon Sequestration
Zhao Feng, Xin-Yang Liu, Meet Hemant Parikh +4
Geological Carbon Sequestration (GCS) has emerged as a promising strategy for mitigating global warming, yet its effectiveness heavily depends on accurately characterizing subsurfa…
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…
CoNFiLD-inlet: Synthetic Turbulence Inflow Using Generative Latent Diffusion Models with Neural Fields
Xin-Yang Liu, Meet Hemant Parikh, Xiantao Fan +4
Eddy-resolving turbulence simulations require stochastic inflow conditions that accurately replicate the complex, multi-scale structures of turbulence. Traditional recycling-based…
CoNFiLD: Conditional Neural Field Latent Diffusion Model Generating Spatiotemporal Turbulence
Pan Du, Meet Hemant Parikh, Xiantao Fan +2
This study introduces the Conditional Neural Field Latent Diffusion (CoNFiLD) model, a novel generative learning framework designed for rapid simulation of intricate spatiotemporal…
Asynchronous Parallel Reinforcement Learning for Optimizing Propulsive Performance in Fin Ray Control
Xin-Yang Liu, Dariush Bodaghi, Qian Xue +2
Fish fin rays constitute a sophisticated control system for ray-finned fish, facilitating versatile locomotion within complex fluid environments. Despite extensive research on the…
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…