11 citations · 27 across the 9 of their papers we have counts for
9 papers
Neural Differentiable Modeling with Diffusion-Based Super-resolution for Two-Dimensional Spatiotemporal Turbulence
Xiantao Fan, Deepak Akhare, Jian-Xun Wang
Simulating spatiotemporal turbulence with high fidelity remains a cornerstone challenge in computational fluid dynamics (CFD) due to its intricate multiscale nature and prohibitive…
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…
DiffHybrid-UQ: Uncertainty Quantification for Differentiable Hybrid Neural Modeling
Deepak Akhare, Tengfei Luo, Jian-Xun Wang
The hybrid neural differentiable models mark a significant advancement in the field of scientific machine learning. These models, integrating numerical representations of known phy…
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…
Probabilistic Physics-integrated Neural Differentiable Modeling for Isothermal Chemical Vapor Infiltration Process
Deepak Akhare, Zeping Chen, Richard Gulotty +2
Chemical vapor infiltration (CVI) is a widely adopted manufacturing technique used in producing carbon-carbon and carbon-silicon carbide composites. These materials are especially…