# Kalman-Bucy-Informed Neural Network for System Identification

arXiv: https://arxiv.org/abs/2210.03424 · HTML: https://mlsanity.com/paper/2210.03424/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2022.

## Built on by (most cited first, 3 of 3)

- [PINN Training using Biobjective Optimization: The Trade-off between Data Loss and Residual Loss](https://mlsanity.com/paper/2302.01810/) (2023)
- [Assumed Density Filtering and Smoothing with Neural Network Surrogate Models](https://mlsanity.com/paper/2511.09016/) (2025)
- [Identifying Ordinary Differential Equations for Data-efficient Model-based Reinforcement Learning](https://mlsanity.com/paper/2406.19817/) (2024)

## Abstract (arXiv)

Identifying parameters in a system of nonlinear, ordinary differential equations is vital for designing a robust controller. However, if the system is stochastic in its nature or if only noisy measurements are available, standard optimization algorithms for system identification usually fail. We present a new approach that combines the recent advances in physics-informed neural networks and the well-known achievements of Kalman filters in order to find parameters in a continuous-time system with noisy measurements. In doing so, our approach allows estimating the parameters together with the mean value and covariance matrix of the system's state vector. We show that the method works for complex systems by identifying the parameters of a double pendulum.