Corpus ID: 236956986

Iterative Symbolic Regression for Learning Transport Equations

@inproceedings{Ansari2021IterativeSR,
  title={Iterative Symbolic Regression for Learning Transport Equations},
  author={Mehrad Ansari and Heta A. Gandhi and David George Foster and Andrew D. White},
  year={2021}
}
  • Mehrad Ansari, Heta A. Gandhi, +1 author Andrew D. White
  • Published 2021
  • Physics
Computational fluid dynamics (CFD) analysis is widely used in engineering. Although CFD calculations are accurate, the computational cost associated with complex systems makes it difficult to obtain empirical equations between system variables. Here we combine active learning (AL) and symbolic regression (SR) to get a symbolic equation for system variables from CFD simulations. Gaussian process regression-based AL allows for automated selection of variables by selecting the most instructive… Expand

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References

SHOWING 1-10 OF 54 REFERENCES
Improved Junction Body Flow Modeling Through Data-Driven Symbolic Regression
A novel data-driven turbulence modeling framework is presented and applied to the problem of junction body flow. In particular, a symbolic regression approach is used to find nonlinear analyticalExpand
Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression
A novel deterministic symbolic regression method SpaRTA (Sparse Regression of Turbulent Stress Anisotropy) is introduced to infer algebraic stress models for the closure of RANS equations directlyExpand
A new formulation for symbolic regression to identify physico-chemical laws from experimental data
TLDR
The methodology was proven to be successful in identifying the correct physical models describing the relationship between shear stress and shear rate for both Newtonian and non-Newtonian fluids, and simple kinetic laws of chemical reactions. Expand
Solving fluid flow problems using semi-supervised symbolic regression on sparse data
TLDR
It is shown how symbolic regression machine learning methods, based on genetic programming, can be used to solve fluid flow problems, and good agreement with literature results is shown on the fluid drag experienced by ellipsoidal and spherocylinder particles of arbitrary aspect ratio. Expand
An Automated Machine Learning-Genetic Algorithm Framework With Active Learning for Design Optimization
TLDR
The framework introduced in this study reduces the need for technical expertise in training ML models for optimization while also reducing the number of simulations needed for performing surrogate-based design optimization. Expand
Automated reverse engineering of nonlinear dynamical systems
TLDR
This work introduces for the first time a method that can automatically generate symbolic equations for a nonlinear coupled dynamical system directly from time series data, applicable to any system that can be described using sets of ordinary nonlinear differential equations. Expand
Active learning for modeling and prediction of dynamical fluid processes
TLDR
An active learning method is proposed to efficiently design informative training data for accurate prediction of the flow rate curve of a stroke for reciprocating multiphase pumps and an evaluation criterion is designed to implement the active learning procedure efficiently. Expand
Feature engineering and symbolic regression methods for detecting hidden physics from sparse sensor observation data
In this study we put forth a modular approach for distilling hidden flow physics in discrete and sparse observations. To address functional expressiblity, a key limitation of the black-box machineExpand
Data-driven discovery of partial differential equations
TLDR
The sparse regression method is computationally efficient, robust, and demonstrated to work on a variety of canonical problems spanning a number of scientific domains including Navier-Stokes, the quantum harmonic oscillator, and the diffusion equation. Expand
AI Feynman: A physics-inspired method for symbolic regression
TLDR
This work develops a recursive multidimensional symbolic regression algorithm that combines neural network fitting with a suite of physics-inspired techniques and improves the state-of-the-art success rate. Expand
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