Multi-objective symbolic regression for physics-aware dynamic modeling

Kubalı́k, Jiřı́; Derner, Erik; Babuška, Robert

doi:10.1016/j.eswa.2021.115210

Cited by 21 publications

(14 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast, the SR system LGGA 62 reduces the search space with more specific physical knowledge formulated as mathematical constraints. Another example is the Multi-objective SR system for dynamic models 63 , which considers knowledge about steady-state characteristics or local behavior to direct the search efforts towards a logical result.…”

Section: Sr Methodsmentioning

confidence: 99%

A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledge

Keren

Liberzon

Lazebnik

2023

Sci Rep

View full text Add to dashboard Cite

Discovering a meaningful symbolic expression that explains experimental data is a fundamental challenge in many scientific fields. We present a novel, open-source computational framework called Scientist-Machine Equation Detector (SciMED), which integrates scientific discipline wisdom in a scientist-in-the-loop approach, with state-of-the-art symbolic regression (SR) methods. SciMED combines a wrapper selection method, that is based on a genetic algorithm, with automatic machine learning and two levels of SR methods. We test SciMED on five configurations of a settling sphere, with and without aerodynamic non-linear drag force, and with excessive noise in the measurements. We show that SciMED is sufficiently robust to discover the correct physically meaningful symbolic expressions from the data, and demonstrate how the integration of domain knowledge enhances its performance. Our results indicate better performance on these tasks than the state-of-the-art SR software packages , even in cases where no knowledge is integrated. Moreover, we demonstrate how SciMED can alert the user about possible missing features, unlike the majority of current SR systems.

show abstract

Section: Sr Methodsmentioning

confidence: 99%

A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledge

Keren

Liberzon

Lazebnik

2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…In [14], explicit approximations of widely used hydraulics, the Colebrook equation for flow friction obtained with SR method are considered. In [15], authors proposed two-phase bi-objective symbolic regression method and discussed how to choose the model that fit the training data as precisely as possible and is consistent with the prior knowledge about the system given in the form of nonlinear inequality and equality constraints. In [16], the authors, using SR, reconstruct the pressure and the forcing field for a weakly turbulent fluid flow only when the velocity field is known.…”

Section: Methods and Modelsmentioning

confidence: 99%

Data Analysis and Symbolic Regression Models for Predicting CO and NOx Emissions from Gas Turbines

Kochueva

Nikolskii

2021

Computation

View full text Add to dashboard Cite

Predictive emission monitoring systems (PEMS) are software solutions for the validation and supplementation of costly continuous emission monitoring systems for natural gas electrical generation turbines. The basis of PEMS is that of predictive models trained on past data to estimate emission components. The gas turbine process dataset from the University of California at Irvine open data repository has initiated a challenge of sorts to investigate the quality of models of various machine learning methods to build a model for predicting CO and NOx emissions depending on ambient variables and the parameters of the technological process. The novelty and features of this paper are: (i) a contribution to the study of the features of the open dataset on CO and NOx emissions for gas turbines, which will enable one to more objectively compare different machine learning methods for further research; (ii) for the first time for the CO and NOx emissions, a model based on symbolic regression and a genetic algorithm is presented—the advantage of this being the transparency of the influence of factors and the interpretability of the model; (iii) a new classification model based on the symbolic regression model and fuzzy inference system is proposed. The coefficients of determination of the developed models are: R2=0.83 for NOx emissions, R2=0.89 for CO emissions.

show abstract

“…Recently, OLS-based genetic programming was introduced for system diagnosis based on regression [8]. As the value of feature extraction methods has increased in the last few years, symbolic regression based feature has also been introduced and used in modelling [9] and control studies [10] and prediction [11].…”

Section: Selami Beyhanmentioning

confidence: 99%

Symbolic Regression Based Feature Extraction of Shallow Neural-Networks for Identification and Prediction

Beyhan¹

2022

AIS

View full text Add to dashboard Cite

This paper proposes a feature extraction method to improve the performance of shallow neural-network models with less number of parameters to apply especially on embedded system design at remote applications. Feature extraction method is designed using fuzzy c-means clustering based fuzzy system design cascaded a layer of symbolic operators and functions, respectively. During the training stage of neural-networks, symbolic operators and functions are selected using random-learning theory with the unity internal weights such that based on the prediction performance, optimal sequences are recorded for feature extraction to be utilized on testing phase. Extracted features are here used to empower the single-layer neural-network (SLNN) with sigmoid hyperbolic activation functions, functional-link neural- network (FLNN) with Chebyshev polynomials and Pi-Sigma higher-order neural-network (PSNN) with sigmoid activation functions, respectively. The internal and output parameters of the appended shallow neural-networks are optimized using batch optimization methods. Proposed regression models are first tested on identification of an artificial discrete-time dynamic system and real-time inverted pendulum then also for prediction of the sunspot time-series and traffic density estimation. As a result, the prediction performance of shallow neural networks is improved to be used in future applications.

show abstract

Multi-objective symbolic regression for physics-aware dynamic modeling

Cited by 21 publications

References 30 publications

A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledge

A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledge

Data Analysis and Symbolic Regression Models for Predicting CO and NOx Emissions from Gas Turbines

Symbolic Regression Based Feature Extraction of Shallow Neural-Networks for Identification and Prediction

Contact Info

Product

Resources

About