Results 11 to 20 of about 12,301 (257)
Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts [PDF]
Nature Communications, 2020 Baicheng Weng, Qingde Sun, Yanfa Yan
exaly +2 more sources
Application of symbolic regression for constitutive modeling of plastic deformation
Applications in Engineering Science, 2021 In numerical process simulations, in-depth knowledge about material behavior during processing in the form of trustworthy material models is crucial.
Evgeniya Kabliman +4 more
doaj +1 more source
Adaptive Chaotic Marine Predators Hill Climbing Algorithm for Large-Scale Design Optimizations
IEEE Access, 2023 Meta-heuristic algorithms have been effectively employed to tackle a wide range of optimisation issues, including structural engineering challenges.
Amin Abdollahi Dehkordi +3 more
doaj +1 more source
Prediction of microscopic residual stresses using genetic programming
Applications in Engineering Science, 2023 Metallurgical manufacturing processes commonly used in the industry (rolling, extrusion, shaping, machining, etc.) usually cause residual stress development which can remain after thermal heat treatments.
Laura Millán +7 more
doaj +1 more source
IET Collaborative Intelligent Manufacturing, 2021 Predictive models are increasingly deployed within smart manufacturing for the control of industrial plants. With this arises, the need for long‐term monitoring of model performance and adaptation of models if surrounding conditions change and the ...
Florian Bachinger +2 more
doaj +1 more source
Symbolic Regression Methods for Reinforcement Learning
IEEE Access, 2021 Reinforcement learning algorithms can solve dynamic decision-making and optimal control problems. With continuous-valued state and input variables, reinforcement learning algorithms must rely on function approximators to represent the value function and ...
Jiri Kubalik +3 more
doaj +1 more source
Regression Models for Symbolic Interval-Valued Variables
Entropy, 2021 This paper presents new approaches to fit regression models for symbolic internal-valued variables, which are shown to improve and extend the center method suggested by Billard and Diday and the center and range method proposed by Lima-Neto, E.A.and De ...
Jose Emmanuel Chacón +1 more
doaj +1 more source
Controllable Neural Symbolic Regression
, 2023 In symbolic regression, the goal is to find an analytical expression that accurately fits experimental data with the minimal use of mathematical symbols such as operators, variables, and constants. However, the combinatorial space of possible expressions can make it challenging for traditional evolutionary algorithms to find the correct expression in a
Bendinelli, Tommaso +2 more
openaire +3 more sources
Matching Large Biomedical Ontologies Using Symbolic Regression Using Symbolic Regression
Journal of Data Intelligence, 2022 The problem of ontology matching consists of finding the semantic correspondences between two ontologies that, although belonging to the same domain, have been developed separately. Ontology matching methods are of great importance today since they allow us to find the pivot points from which an automatic data integration process can be established ...
Martinez-Gil, Jorge +3 more
openaire +2 more sources
, 2023 Automating scientific discovery has been a grand goal of Artificial Intelligence (AI) and will bring tremendous societal impact. Learning symbolic expressions from experimental data is a vital step in AI-driven scientific discovery. Despite exciting progress, most endeavors have focused on the horizontal discovery paths, i.e., they directly search for ...
Jiang, Nan, Nasim, Md, Xue, Yexiang
openaire +2 more sources