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Digital Twin

APOD: Agentic Population ODE Discovery for Pharmacological Digital Twins

US & West ·

APOD is a language-model agent that iteratively reasons over biological knowledge and fit diagnostics in an open-ended search space to discover a population digital twin, a shared ODE system with between-subject variability. On synthetic pharmacokinetic and tumor-dynamics benchmarks it recovered ground-truth structures in 94-100% of runs, 12-fold faster in median than a library-based search.

Why it matters

Automating model-structure identification from noisy data matters for building digital twins in any domain.

Relevant to DT products

Original headline
APOD: reasoning-guided agentic population ordinary differential equation discovery for pharmacological digital twins
Read the original: arXiv API — полнотекстовый поиск "digital twin"

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