Stochastic Process Learning via Operator Flow Matching Article Swipe

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Yaozhong Shi , Zachary E. Ross , Domniki Asimaki , Kamyar Azizzadenesheli ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2501.04126

Expanding on neural operators, we propose a novel framework for stochastic process learning across arbitrary domains. In particular, we develop operator flow matching (OFM) for learning stochastic process priors on function spaces. OFM provides the probability density of the values of any collection of points and enables mathematically tractable functional regression at new points with mean and density estimation. Our method outperforms state-of-the-art models in stochastic process learning, functional regression, and prior learning.

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Matching (statistics) Operator (biology) Computer science Process (computing) Flow (mathematics) Mathematics Statistics Programming language Chemistry Biochemistry Geometry Repressor Gene Transcription factor

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2501.04126
PDF: https://arxiv.org/pdf/2501.04126
OA Status: green
Related Works: 10
OpenAlex ID: https://openalex.org/W4406231028

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OpenAlex ID: https://openalex.org/W4406231028

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DOI: https://doi.org/10.48550/arxiv.2501.04126

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Title: Stochastic Process Learning via Operator Flow Matching

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-01-07

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Authors: Yaozhong Shi, Zachary E. Ross, Domniki Asimaki, Kamyar Azizzadenesheli

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Landing page: https://arxiv.org/abs/2501.04126

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PDF URL: https://arxiv.org/pdf/2501.04126

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Concepts: Matching (statistics), Operator (biology), Computer science, Process (computing), Flow (mathematics), Mathematics, Statistics, Programming language, Chemistry, Biochemistry, Geometry, Repressor, Gene, Transcription factor

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Related works (count): 10

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