Symbolic Regression on Sparse and Noisy Data with Gaussian Processes
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2023
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866929536511246336 |
|---|---|
| author | Hsin, Junette Agarwal, Shubhankar Thorpe, Adam Sentis, Luis Fridovich-Keil, David |
| author_facet | Hsin, Junette Agarwal, Shubhankar Thorpe, Adam Sentis, Luis Fridovich-Keil, David |
| contents | In this paper, we address the challenge of deriving dynamical models from sparse and noisy data. High-quality data is crucial for symbolic regression algorithms; limited and noisy data can present modeling challenges. To overcome this, we combine Gaussian process regression with a sparse identification of nonlinear dynamics (SINDy) method to denoise the data and identify nonlinear dynamical equations. Our approach GPSINDy offers improved robustness with sparse, noisy data compared to SINDy alone. We demonstrate its effectiveness on simulation data from Lotka-Volterra and unicycle models and hardware data from an NVIDIA JetRacer system. We show superior performance over baselines including more than 50% improvement over SINDy and other baselines in predicting future trajectories from noise-corrupted and sparse 5 Hz data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_11076 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Symbolic Regression on Sparse and Noisy Data with Gaussian Processes Hsin, Junette Agarwal, Shubhankar Thorpe, Adam Sentis, Luis Fridovich-Keil, David Machine Learning Systems and Control In this paper, we address the challenge of deriving dynamical models from sparse and noisy data. High-quality data is crucial for symbolic regression algorithms; limited and noisy data can present modeling challenges. To overcome this, we combine Gaussian process regression with a sparse identification of nonlinear dynamics (SINDy) method to denoise the data and identify nonlinear dynamical equations. Our approach GPSINDy offers improved robustness with sparse, noisy data compared to SINDy alone. We demonstrate its effectiveness on simulation data from Lotka-Volterra and unicycle models and hardware data from an NVIDIA JetRacer system. We show superior performance over baselines including more than 50% improvement over SINDy and other baselines in predicting future trajectories from noise-corrupted and sparse 5 Hz data. |
| title | Symbolic Regression on Sparse and Noisy Data with Gaussian Processes |
| topic | Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2309.11076 |