arXiv:2509.02971v2 Announce Type: replace-cross Abstract: Flow-based generative models can face numerical challenges on scientific data with multiscale Fourier spectra, often producing large errors at fine scales. We approach this problem within the flow matching and stochastic interpolants framework, through the principled design of noise distributions and interpolation schedules. Working in function space ensures that the generative model remains well defined as the resolution is refined; the Lipschitz regularity of the drift is important to both this function-space well-posedness and the in
This research addresses a critical limitation in generative AI's application to complex scientific data, which is becoming more prevalent as AI is integrated into scientific discovery workflows.
Improved generative models for scientific data can accelerate research, drug discovery, and materials science, leading to breakthroughs in various fields dependent on high-fidelity simulations.
Flow-based generative models become more reliable and accurate for multiscale scientific data, potentially broadening their application beyond current limitations at fine scales.
- · AI researchers
- · Scientific computing sector
- · Pharmaceutical industry
- · Materials science research
- · Traditional simulation methods
- · Generative models without robust multiscale handling
More accurate and efficient AI-driven scientific simulations become possible.
Reduced time and cost for R&D in areas like drug discovery and climate modeling due to better generative models.
New scientific discoveries enabled by AI's ability to model and predict complex multiscale phenomena with higher fidelity.
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Read at arXiv cs.LG