arXiv:2607.02088v1 Announce Type: new Abstract: We propose an improved Fourier Neural Operator (FNO) for modeling two-dimensional Rayleigh-B\'enard convection by predicting time increments instead of full solutions, achieving higher accuracy than a standard FNO baseline. The resulting model is compact (314k parameters, 1.26 MB) and fast (7 ms inference), while maintaining similar accuracy as demonstrated in previous benchmarks. We show that although FNOs generalize to finer meshes, accuracy remains limited by the resolution of the training data.
The continuous advancements in AI and computational fluid dynamics (CFD) are pushing the boundaries of scientific simulations, making breakthroughs in areas like convection modeling timely.
This development allows for more accurate, compact, and faster simulations of complex physical phenomena, which is crucial for various scientific and engineering applications.
The ability to model physical systems like Rayleigh-Bénard convection with higher accuracy and efficiency using AI-driven methods reduces computational burden and accelerates research and development cycles.
- · Scientific research institutions
- · Engineering design firms
- · Climate scientists
- · AI model developers
- · Traditional CFD software developers (if they fail to integrate AI)
- · Organizations reliant on high-cost, time-intensive simulations
Improved predictive models for weather, climate, and material science are enabled by more efficient simulation techniques.
Reduced computational costs and time for R&D in areas involving fluid dynamics could accelerate innovation in sectors like aerospace and energy.
The democratization of advanced simulation capabilities could lower barriers to entry for smaller research groups and companies in complex engineering fields.
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Read at arXiv cs.LG