Forecasting Without Full Identification: Reliability and Computational Cost in Clot-Growth Simulation
We forecast clot growth from sparse early measurements with incomplete biochemical information. Using 1,000 simulated trajectories, we compare inverse search, which weights candidate inputs by their agreement with early observations, with a direct multilayer perceptron (MLP) predicting future clot size. Inverse search is more accurate when tissue factor alone, or tissue factor and fibrinogen, are unknown. When all five inputs are unknown, direct prediction achieves 2.16% pooled relative $L_2$ forecast error, outperforming search with 8,192 candidates using a forward MLP (3.25%) or latent neural ordinary differential equation model (3.36%). A conditional error bound separates candidate dis...
| Authors | Lennon J. Shikhman, Lily DiPaulo, Ying Qian et al. |
|---|---|
| Primary category | cs.LG |
| Published | 8 August 2026 |
| Last updated at source | 13 October 2026 |
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