# Leveraging Predictive Models for Adaptive Sampling of Spatiotemporal Fluid Processes

arXiv: https://arxiv.org/abs/2304.00732 · HTML: https://mlsanity.com/paper/2304.00732/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 30 references that are in the map.
- First posted on arXiv 3 Apr 2023.

## Builds on (most cited first, 15 of 15)

- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [Machine Learning for Fluid Mechanics](https://mlsanity.com/paper/1905.11075/) (2019)
- [Neural Dynamics on Complex Networks](https://mlsanity.com/paper/1908.06491/) (2019)
- [Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control](https://mlsanity.com/paper/2106.12782/) (2021)
- [NODEO: A Neural Ordinary Differential Equation Based Optimization Framework for Deformable Image Registration](https://mlsanity.com/paper/2108.03443/) (2021)
- [Combining Machine Learning with Knowledge-Based Modeling for Scalable Forecasting and Subgrid-Scale Closure of Large, Complex, Spatiotemporal Systems](https://mlsanity.com/paper/2002.05514/) (2020)
- [Online Dynamics Learning for Predictive Control with an Application to Aerial Robots](https://mlsanity.com/paper/2207.09344/) (2022)
- [How Entropic Regression Beats the Outliers Problem in Nonlinear System Identification](https://mlsanity.com/paper/1905.08061/) (2019)
- [Knowledge-Based Learning of Nonlinear Dynamics and Chaos](https://mlsanity.com/paper/2010.03415/) (2020)
- [Bridging the Gap: Machine Learning to Resolve Improperly Modeled Dynamics](https://mlsanity.com/paper/2008.12642/) (2020)
- [Reinforcement Learning with Non-uniform State Representations for Adaptive Search](https://mlsanity.com/paper/1906.06588/) (2019)
- [Adaptive Sampling: Algorithmic vs. Human Waypoint Selection](https://mlsanity.com/paper/2104.11962/) (2021)
- [Learning and Leveraging Features in Flow-Like Environments to Improve Situational Awareness](https://mlsanity.com/paper/2109.06107/) (2021)
- [Autonomous Robotic Mapping of Fragile Geologic Features](https://mlsanity.com/paper/2105.01287/) (2021)
- [Scalable Multirobot Planning for Informed Spatial Sampling](https://mlsanity.com/paper/2105.10018/) (2021)

## Abstract (arXiv)

Persistent monitoring of a spatiotemporal fluid process requires data sampling and predictive modeling of the process being monitored. In this paper we present PASST algorithm: Predictive-model based Adaptive Sampling of a Spatio-Temporal process. PASST is an adaptive robotic sampling algorithm that leverages predictive models to efficiently and persistently monitor a fluid process in a given region of interest. Our algorithm makes use of the predictions from a learned prediction model to plan a path for an autonomous vehicle to adaptively and efficiently survey the region of interest. In turn, the sampled data is used to obtain better predictions by giving an updated initial state to the predictive model. For predictive model, we use Knowledged-based Neural Ordinary Differential Equations to train models of fluid processes. These models are orders of magnitude smaller in size and run much faster than fluid data obtained from direct numerical simulations of the partial differential equations that describe the fluid processes or other comparable computational fluids models. For path planning, we use reinforcement learning based planning algorithms that use the field predictions as reward functions. We evaluate our adaptive sampling path planning algorithm on both numerically simulated fluid data and real-world nowcast ocean flow data to show that we can sample the spatiotemporal field in the given region of interest for long time horizons. We also evaluate PASST algorithm's generalization ability to sample from fluid processes that are not in the training repertoire of the learned models.