# Estimate Epidemiological Parameters given Partial Observations based on Algebraically Observable PINNs

arXiv: https://arxiv.org/abs/2407.12598 · HTML: https://mlsanity.com/paper/2407.12598/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 17 Jul 2024.

## Builds on (most cited first, 5 of 5)

- [Sobolev Training for Neural Networks](https://mlsanity.com/paper/1706.04859/) (2017)
- [Hyper-Parameter Optimization: A Review of Algorithms and Applications](https://mlsanity.com/paper/2003.05689/) (2020)
- [Hyper-parameter tuning of physics-informed neural networks: Application to Helmholtz problems](https://mlsanity.com/paper/2205.06704/) (2022)
- [A Modified PINN Approach for Identifiable Compartmental Models in Epidemiology with Applications to COVID-19](https://mlsanity.com/paper/2208.01169/) (2022)
- [Algebraic Tools for the Analysis of State Space Models](https://mlsanity.com/paper/1609.07985/) (2016)

## Built on by (most cited first, 1 of 1)

- [Physics-Informed Neural ODEs with Scale-Aware Residuals for Learning Stiff Biophysical Dynamics](https://mlsanity.com/paper/2511.11734/) (2025)

## Abstract (arXiv)

In this study, we considered the problem of estimating epidemiological parameters based on physics-informed neural networks (PINNs). In practice, not all trajectory data corresponding to the population estimated by epidemic models can be obtained, and some observed trajectories are noisy. Learning PINNs to estimate unknown epidemiological parameters using such partial observations is challenging. Accordingly, we introduce the concept of algebraic observability into PINNs. The validity of the proposed PINN, named as an algebraically observable PINNs, in terms of estimation parameters and prediction of unobserved variables, is demonstrated through numerical experiments.