1.1k citations · 1.9k 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…
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…
Bayesian Conditional Diffusion Models for Versatile Spatiotemporal Turbulence Generation
Han Gao, Xu Han, Xiantao Fan +4
Turbulent flows have historically presented formidable challenges to predictive computational modeling. Traditional numerical simulations often require vast computational resources…
Bayesian Spline Learning for Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty
Luning Sun, Daniel Zhengyu Huang, Hao Sun +1
Nonlinear dynamics are ubiquitous in science and engineering applications, but the physics of most complex systems is far from being fully understood. Discovering interpretable gov…
An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions
Ehsan Adeli, Luning Sun, Jianxun Wang +1
In this research paper, we study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history, leveraging a database of…
Machine Learning-Assisted Exploration of Thermally Conductive Polymers Based on High-Throughput Molecular Dynamics Simulations
Ruimin Ma, Hanfeng Zhang, Jiaxin Xu +4
Finding amorphous polymers with higher thermal conductivity is important, as they are ubiquitous in heat transfer applications. With recent progress in material informatics, machin…
Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels
Han Gao, Luning Sun, Jian-Xun Wang
High-resolution (HR) information of fluid flows, although preferable, is usually less accessible due to limited computational or experimental resources. In many cases, fluid data a…
A deep-learning based generalized reduced-order model of glottal flow during normal phonation
Yang Zhang, Weili Jiang, Luning Sun +5
This paper proposes a deep-learning based generalized reduced-order model (ROM) that can provide a fast and accurate prediction of the glottal flow during normal phonation. The app…
PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parameterized Steady-State PDEs on Irregular Domain
Han Gao, Luning Sun, Jian-Xun Wang
Recently, the advent of deep learning has spurred interest in the development of physics-informed neural networks (PINN) for efficiently solving partial differential equations (PDE…
Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data
Luning Sun, Jian-Xun Wang
In many applications, flow measurements are usually sparse and possibly noisy. The reconstruction of a high-resolution flow field from limited and imperfect flow information is sig…
Surrogate Modeling for Fluid Flows Based on Physics-Constrained Deep Learning Without Simulation Data
Luning Sun, Han Gao, Shaowu Pan +1
Numerical simulations on fluid dynamics problems primarily rely on spatially or/and temporally discretization of the governing equation into the finite-dimensional algebraic system…