# Latent Neural ODE for Integrating Multi-timescale measurements in Smart Distribution Grids

arXiv: https://arxiv.org/abs/2211.03882 · HTML: https://mlsanity.com/paper/2211.03882/

## Key facts

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

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

- [Balanced Neural ODEs: nonlinear model order reduction and Koopman operator approximations](https://mlsanity.com/paper/2410.10174/) (2024)
- [A General Framework for Uncertainty Quantification via Neural SDE-RNN](https://mlsanity.com/paper/2306.01189/) (2023)
- [Sensor-Aware Joint Data Fusion and State Estimation under Sparse Multi-rate Distribution Grid Sensing](https://mlsanity.com/paper/2610.15156/) (2026)

## Abstract (arXiv)

Under a smart grid paradigm, there has been an increase in sensor installations to enhance situational awareness. The measurements from these sensors can be leveraged for real-time monitoring, control, and protection. However, these measurements are typically irregularly sampled. These measurements may also be intermittent due to communication bandwidth limitations. To tackle this problem, this paper proposes a novel latent neural ordinary differential equations (LODE) approach to aggregate the unevenly sampled multivariate time-series measurements. The proposed approach is flexible in performing both imputations and predictions while being computationally efficient. Simulation results on IEEE 37 bus test systems illustrate the efficiency of the proposed approach.