Revealing Complex Ecological Dynamics via Symbolic Regression
Sign Up to like & getrecommendations! Published in 2019 at "BioEssays"
DOI: 10.1002/bies.201900069
Abstract: Understanding the dynamics of complex ecosystems is a necessary step to maintain and control them. Yet, reverse‐engineering ecological dynamics remains challenging largely due to the very broad class of dynamics that ecosystems may take. Here,… read more here.
Keywords: symbolic regression; complex ecological; dynamics via; regression ... See more keywords
An adaptive GP-based memetic algorithm for symbolic regression
Sign Up to like & getrecommendations! Published in 2020 at "Applied Intelligence"
DOI: 10.1007/s10489-020-01745-w
Abstract: Symbolic regression is a process to find a mathematical expression that represents the relationship between a set of explanatory variables and a measured variable. It has become a best-known problem for GP (genetic programming), as… read more here.
Keywords: symbolic regression; local search; regression; based memetic ... See more keywords
Symbolic regression of uncertainty-resilient inferential sensors for fault diagnostics
Sign Up to like & getrecommendations! Published in 2020 at "IFAC-PapersOnLine"
DOI: 10.1016/j.ifacol.2020.12.582
Abstract: An algorithm is presented for the design of inferential sensors for fault diagnostics in thermal management systems. The algorithm uses input and output sensed system information to improve the detection and isolation of a fault… read more here.
Keywords: symbolic regression; fault diagnostics; sensors fault; uncertainty ... See more keywords
Machine learning control — explainable and analyzable methods
Sign Up to like & getrecommendations! Published in 2020 at "Physica D: Nonlinear Phenomena"
DOI: 10.1016/j.physd.2020.132582
Abstract: Abstract Recently, the term explainable AI came into discussion as an approach to produce models from artificial intelligence which allow interpretation. For a long time, symbolic regression has been used to produce explainable and mathematically… read more here.
Keywords: symbolic regression; machine learning; control; learning control ... See more keywords
Improving Symbolic Regression for Predicting Materials Properties with Iterative Variable Selection.
Sign Up to like & getrecommendations! Published in 2022 at "Journal of chemical theory and computation"
DOI: 10.1021/acs.jctc.2c00281
Abstract: Symbolic regression offers a promising avenue for describing the structure-property relationships of materials with explicit mathematical expressions, yet it meets challenges when the key variables are unclear because of the high complexity of the problems.… read more here.
Keywords: iterative variable; regression; input features; symbolic regression ... See more keywords
Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts
Sign Up to like & getrecommendations! Published in 2020 at "Nature Communications"
DOI: 10.1038/s41467-020-17263-9
Abstract: Symbolic regression (SR) is an approach of interpretable machine learning for building mathematical formulas that best fit certain datasets. In this work, SR is used to guide the design of new oxide perovskite catalysts with… read more here.
Keywords: symbolic regression; simple descriptor; perovskite catalysts; new oxide ... See more keywords
Distilling universal activity descriptors for perovskite catalysts from multiple data sources via multi-task symbolic regression.
Sign Up to like & getrecommendations! Published in 2023 at "Materials horizons"
DOI: 10.1039/d3mh00157a
Abstract: Developing activity descriptors via data-driven machine learning (ML) methods can speed up the design of highly active and low-cost electrocatalysts. Despite the fact that a large amount of activity data for electrocatalysts is stored in… read more here.
Keywords: universal activity; task symbolic; activity descriptors; activity ... See more keywords
Assessment of the effect of the financial crisis on agents’ expectations through symbolic regression
Sign Up to like & getrecommendations! Published in 2017 at "Applied Economics Letters"
DOI: 10.1080/13504851.2016.1218419
Abstract: ABSTRACT Agents’ perceptions on the state of the economy can be affected during economic crises. Tendency surveys are the main source of agents’ expectations. The main objective of this study is to assess the impact… read more here.
Keywords: symbolic regression; financial crisis; crisis agents; agents expectations ... See more keywords
Data-driven discovery of the governing equations for transport in heterogeneous media by symbolic regression and stochastic optimization.
Sign Up to like & getrecommendations! Published in 2023 at "Physical review. E"
DOI: 10.1103/physreve.107.l013301
Abstract: With advances in instrumentation and the tremendous increase in computational power, vast amounts of data are becoming available for many complex phenomena in macroscopically heterogeneous media, particularly those that involve flow and transport processes, which… read more here.
Keywords: governing equations; data driven; stochastic optimization; heterogeneous media ... See more keywords
Hierarchical Symbolic Regression for Identifying Key Physical Parameters Correlated with Bulk Properties of Perovskites.
Sign Up to like & getrecommendations! Published in 2022 at "Physical review letters"
DOI: 10.1103/physrevlett.129.055301
Abstract: Symbolic regression identifies nonlinear, analytical expressions relating materials properties and key physical parameters. However, the pool of expressions grows rapidly with complexity, compromising its efficiency. We tackle this challenge hierarchically: identified expressions are used as… read more here.
Keywords: hierarchical symbolic; physical parameters; key physical; symbolic regression ... See more keywords
Fuzzy Emulated Symbolic Regression for Modelling and Control of Markov Jump Systems With Unknown Transition Rates
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Circuits and Systems II: Express Briefs"
DOI: 10.1109/tcsii.2021.3104874
Abstract: In this brief, a novel fuzzy emulated symbolic regression (FESR) model is proposed for modelling and control of Markov jump systems with unknown transition rates. Conventional symbolic regression model is improved in multi-layered form where… read more here.
Keywords: markov jump; jump systems; control markov; model ... See more keywords