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MMSR: Symbolic regression is a multi-modal information fusion task
Information FusionMathematical formulas are the crystallization of human wisdom in exploring the laws of nature for thousands of years. Describing the complex laws of nature with a concise mathematical formula is a constant pursuit of scientists and a great challenge for ...
Yanjie Li +8 more
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Symbolic Regression for Beyond the Standard Model Physics
Physical Review DWe propose symbolic regression as a powerful tool for the numerical studies of proposed models of physics beyond the Standard Model. In this paper we demonstrate the efficacy of the method on a benchmark model, namely the constrained minimal ...
Shehu AbdusSalam, Steve Abel, M. Romão
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IEEE Transactions on Emerging Topics in Computational Intelligence
Symbolic regression is increasingly important for discovering mathematical models for various prediction tasks. It works by searching for the arithmetic expressions that best represent a target variable using a set of input features.
Baligh M. Al-Helali +3 more
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Symbolic regression is increasingly important for discovering mathematical models for various prediction tasks. It works by searching for the arithmetic expressions that best represent a target variable using a set of input features.
Baligh M. Al-Helali +3 more
semanticscholar +1 more source
Rediscovering the Mullins Effect With Deep Symbolic Regression
International journal of plasticityThe Mullins effect represents a softening phenomenon observed in rubber-like materials and soft biological tissues. It is usually accompanied by many other inelastic effects like for example residual strain and induced anisotropy.
R. Abdusalamov, Jendrik Weise, M. Itskov
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AIAA Journal
This paper addresses the issue of predicting separated flows with Reynolds-averaged Navier–Stokes (RANS) turbulence models, which are essential for many engineering tasks.
Chenyu Wu, Shaoguang Zhang, Yu-Fei Zhang
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This paper addresses the issue of predicting separated flows with Reynolds-averaged Navier–Stokes (RANS) turbulence models, which are essential for many engineering tasks.
Chenyu Wu, Shaoguang Zhang, Yu-Fei Zhang
semanticscholar +1 more source
Bloat and Generalisation in Symbolic Regression
2014Symbolic regression is a common application of genetic programming GP. Increasingly, the GP community is identifying the need to measure the generalisation performance of the models evolved in symbolic regression, and consequently the need to design operators and methods that promote generalisation.
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International Conference on Learning Representations
Symbolic regression, a task discovering the formula best fitting the given data, is typically based on the heuristical search. These methods usually update candidate formulas to obtain new ones with lower prediction errors iteratively.
Zihan Yu, Jingtao Ding, Yong Li
semanticscholar +1 more source
Symbolic regression, a task discovering the formula best fitting the given data, is typically based on the heuristical search. These methods usually update candidate formulas to obtain new ones with lower prediction errors iteratively.
Zihan Yu, Jingtao Ding, Yong Li
semanticscholar +1 more source
Machine learning and symbolic regression investigation on stability of MXene materials
Computational Materials Science, 2021exaly
Extreme Accuracy in Symbolic Regression
2014Although recent advances in symbolic regression (SR) have promoted the field into the early stages of commercial exploitation, the poor accuracy of SR is still plaguing even the most advanced commercial packages today. Users expect to have the correct formula returned, especially in cases with zero noise and only one basis function with minimally ...
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Proceedings of the 10th annual conference companion on Genetic and evolutionary computation, 2008
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