Parameter inference and uncertainty quantification with diffusion models: extending CDI to 2D spatial conditioning

Uncertainty quantification is critical in scientific inverse problems to distinguish identifiable parameters from those that remain ambiguous given available measurements. The conditional diffusion model-based inverse problem solver (CDI) has previously demonstrated effective probabilistic inference...

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Hlavní autoři:	Dmitrii Torbunov, Yihui Ren, Lijun Wu, Yimei Zhu
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	IOP Publishing 2026-01-01
Edice:	Machine Learning: Science and Technology
Témata:	uncertainty quantification inverse problem diffusion models
On-line přístup:	https://doi.org/10.1088/2632-2153/ae7f7b
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