Prior-Fitted Functional Flow: In-Context Generative Models for Pharmacokinetics
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866913045781938176 |
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| author | Ojeda, César Hartung, Niklas Huisinga, Wilhelm Jahn, Tim Kavwele, Purity Kamene Klose, Marian Kumar, Piyush Sánchez, Ramsés J. Faroughy, Darius A. |
| author_facet | Ojeda, César Hartung, Niklas Huisinga, Wilhelm Jahn, Tim Kavwele, Purity Kamene Klose, Marian Kumar, Piyush Sánchez, Ramsés J. Faroughy, Darius A. |
| contents | We introduce Prior-Fitted Functional Flows, a generative foundation model for pharmacokinetics that enables zero-shot population synthesis and individual forecasting without manual parameter tuning. We learn functional vector fields, explicitly conditioned on the sparse, irregular data of an entire study population. This enables the generation of coherent virtual cohorts as well as forecasting of partially observed patient trajectories with calibrated uncertainty. We construct a new open-access literature corpus to inform our priors, and demonstrate state-of-the-art predictive accuracy on extensive real-world datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_17670 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Prior-Fitted Functional Flow: In-Context Generative Models for Pharmacokinetics Ojeda, César Hartung, Niklas Huisinga, Wilhelm Jahn, Tim Kavwele, Purity Kamene Klose, Marian Kumar, Piyush Sánchez, Ramsés J. Faroughy, Darius A. Machine Learning We introduce Prior-Fitted Functional Flows, a generative foundation model for pharmacokinetics that enables zero-shot population synthesis and individual forecasting without manual parameter tuning. We learn functional vector fields, explicitly conditioned on the sparse, irregular data of an entire study population. This enables the generation of coherent virtual cohorts as well as forecasting of partially observed patient trajectories with calibrated uncertainty. We construct a new open-access literature corpus to inform our priors, and demonstrate state-of-the-art predictive accuracy on extensive real-world datasets. |
| title | Prior-Fitted Functional Flow: In-Context Generative Models for Pharmacokinetics |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2604.17670 |