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pro vyhledávání: '"Fan, Xiantao"'
Simulating spatiotemporal turbulence with high fidelity remains a cornerstone challenge in computational fluid dynamics (CFD) due to its intricate multiscale nature and prohibitive computational demands. Traditional approaches typically employ closur
Externí odkaz:
http://arxiv.org/abs/2406.20047
This study introduces the Conditional Neural Field Latent Diffusion (CoNFiLD) model, a novel generative learning framework designed for rapid simulation of intricate spatiotemporal dynamics in chaotic and turbulent systems within three-dimensional ir
Externí odkaz:
http://arxiv.org/abs/2403.05940
Turbulent flows have historically presented formidable challenges to predictive computational modeling. Traditional numerical simulations often require vast computational resources, making them infeasible for numerous engineering applications. As an
Externí odkaz:
http://arxiv.org/abs/2311.07896
Autor:
Fan, Xiantao, Wang, Jian-Xun
Solving complex fluid-structure interaction (FSI) problems, which are described by nonlinear partial differential equations, is crucial in various scientific and engineering applications. Traditional computational fluid dynamics based solvers are ina
Externí odkaz:
http://arxiv.org/abs/2303.12971
Publikováno v:
In Computer Methods in Applied Mechanics and Engineering 1 January 2025 433 Part A
Publikováno v:
In Computer Methods in Applied Mechanics and Engineering 1 July 2024 427
Publikováno v:
In International Journal of Mechanical Sciences 1 July 2024 273
Autor:
Fan, Xiantao, Wang, Jian-Xun
Publikováno v:
In Journal of Computational Physics 1 January 2024 496
Publikováno v:
In Energy 15 July 2022 251
Publikováno v:
In Computers and Electronics in Agriculture March 2022 194