Learning from Imperfect Data: Robust Inference of Dynamic Systems using Simulation-based Generative Model
This addresses the problem of robust system inference for researchers and engineers dealing with imperfect data in fields like scientific research and engineered systems, representing an incremental improvement through hybrid methods.
The paper tackles the challenge of inferring nonlinear dynamic systems from noisy, sparse, or partially observable data by proposing SiGMoID, a simulation-based generative model that integrates physics-informed neural networks and Wasserstein GANs to quantify noise, estimate parameters, and infer unobserved components, demonstrating effectiveness in realistic experiments.
System inference for nonlinear dynamic models, represented by ordinary differential equations (ODEs), remains a significant challenge in many fields, particularly when the data are noisy, sparse, or partially observable. In this paper, we propose a Simulation-based Generative Model for Imperfect Data (SiGMoID) that enables precise and robust inference for dynamic systems. The proposed approach integrates two key methods: (1) physics-informed neural networks with hyper-networks that constructs an ODE solver, and (2) Wasserstein generative adversarial networks that estimates ODE parameters by effectively capturing noisy data distributions. We demonstrate that SiGMoID quantifies data noise, estimates system parameters, and infers unobserved system components. Its effectiveness is validated validated through realistic experimental examples, showcasing its broad applicability in various domains, from scientific research to engineered systems, and enabling the discovery of full system dynamics.