August 16, 2025SIAM ReviewOpen Access

Diffusion Models for Generative Artificial Intelligence: An Introduction for Applied Mathematicians

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Diffusion models produce realistic synthetic outputs in generative artificial intelligence, improving applications like image generation.
Performance is supported by methodologies including adding noise and learning to reverse it, resulting in new outputs.
This introduction derives mathematical principles underlying diffusion models in generative AI for applied mathematicians and statisticians.
The findings may serve as both educational tools for teaching courses in stochastic processes and computational experiments for further study.

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This introduction describes diffusion models in generative artificial intelligence, highlighting their role in image generation and mathematical foundations.

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Higham et al. (2025) studied this question.

synapsesocial.com/papers/68a368710a429f797332d211 https://doi.org/10.1137/23m1626232

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Implication

This introduction describes diffusion models in generative artificial intelligence, highlighting their role in image generation and mathematical foundations.

Diffusion Models for Generative Artificial Intelligence: An Introduction for Applied Mathematicians

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Discussion

Implication

Cite This Study

Diffusion Models for Generative Artificial Intelligence: An Introduction for Applied Mathematicians

Key Points

Discussion

Implication

Cite This Study

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