33 citations · 61 across the 7 of their papers we have counts for
7 papers
Denoising diffusion and latent diffusion models for physics field simulations
Yuan Jia, Chi Zhang, Hao Ma +3
Accurate prediction of physical fields is critical in various engineering applications, including thermal management in electronic systems, airfoil shape optimization in aerospace,…
Sloshing suppression with a controlled elastic baffle via deep reinforcement learning and SPH simulation
Mai Ye, Yaru Ren, Silong Zhang +3
This study employed smoothed particle hydrodynamics (SPH) as the numerical environment, integrated with deep reinforcement learning (DRL) real-time control algorithms to optimize t…
DRLinSPH: An open-source platform using deep reinforcement learning and SPHinXsys for fluid-structure-interaction problems
Mai Ye, Hao Ma, Yaru Ren +3
Fluid-structure interaction (FSI) problems are characterized by strong nonlinearities arising from complex interactions between fluids and structures. These pose significant challe…
Heat Conduction Plate Layout Optimization using Physics-driven Convolutional Neural Networks
Hao Ma, Yang Sun, Mario Chiarelli
The layout optimization of the heat conduction is essential during design in engineering, especially for thermal sensible products. When the optimization algorithm iteratively eval…
Physics-driven Learning of the Steady Navier-Stokes Equations using Deep Convolutional Neural Networks
Hao Ma, Yuxuan Zhang, Nils Thuerey +2
Recently, physics-driven deep learning methods have shown particular promise for the prediction of physical fields, especially to reduce the dependency on large amounts of pre-comp…
Generative Adversarial Networks with Physical Evaluators for Spray Simulation of Pintle Injector
Hao Ma, Botao Zhang, Chi Zhang +1
Due to the adjustable geometry, pintle injectors are specially suitable for the liquid rocket engines which require a widely throttleable range. While applying the conventional com…