# Physically Consistent Multiple-Step Data-Driven Predictions Using Physics-based Filters

arXiv: https://arxiv.org/abs/2303.09437 · HTML: https://mlsanity.com/paper/2303.09437/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2023.

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

- [UniPINN: A Unified PINN Framework for Multi-task Learning of Diverse Navier-Stokes Equations](https://mlsanity.com/paper/2603.10466/) (2026)

## Abstract (arXiv)

(Extended Version) Data-driven control can facilitate the rapid development of controllers, offering an alternative to conventional approaches. In order to maintain consistency between any known underlying physical laws and a data-driven decision-making process, preprocessing of raw data is necessary to account for measurement noise and any inconsistencies it may introduce. In this paper, we present a physics-based filter to achieve this and demonstrate its effectiveness through practical applications, using real-world datasets collected in a building on the Ecole Polytechnique Federale de Lausanne (EPFL) campus. Two distinct use cases are explored: indoor temperature control and demand response bidding.