Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model

TL;DR

This paper introduces EIDGM, a novel deep generative modeling approach that accurately estimates system parameters from repeated cross-sectional data, overcoming heterogeneity challenges in fields like biology and economics.

Contribution

The paper presents EIDGM, a new emulator-informed deep generative model that effectively estimates differential equation parameters from heterogeneous RCS data, a task difficult for traditional methods.

Findings

EIDGM outperforms conventional methods in parameter estimation accuracy.

EIDGM successfully captures diverse parameter distributions in biological data.

The approach is applicable to various dynamical systems, including the Lorenz system.

Abstract

Differential equations (DEs) are crucial for modeling the evolution of natural or engineered systems. Traditionally, the parameters in DEs are adjusted to fit data from system observations. However, in fields such as politics, economics, and biology, available data are often independently collected at distinct time points from different subjects (i.e., repeated cross-sectional (RCS) data). Conventional optimization techniques struggle to accurately estimate DE parameters when RCS data exhibit various heterogeneities, leading to a significant loss of information. To address this issue, we propose a new estimation method called the emulator-informed deep-generative model (EIDGM), designed to handle RCS data. Specifically, EIDGM integrates a physics-informed neural network-based emulator that immediately generates DE solutions and a Wasserstein generative adversarial network-based parameter generator that can effectively mimic the RCS data. We evaluated EIDGM on exponential growth, logistic population models, and the Lorenz system, demonstrating its superior ability to accurately capture parameter distributions. Additionally, we applied EIDGM to an experimental dataset of Amyloid beta 40 and beta 42, successfully capturing diverse parameter distribution shapes. This shows that EIDGM can be applied to model a wide range of systems and extended to uncover the operating principles of systems based on limited data.