Results 21 to 30 of about 6,391,262 (321)

SymbolNet: neural symbolic regression with adaptive dynamic pruning for compression [PDF]

Machine Learning: Science and Technology
Compact symbolic expressions have been shown to be more efficient than neural network (NN) models in terms of resource consumption and inference speed when implemented on custom hardware such as field-programmable gate arrays (FPGAs), while maintaining ...
Ho Fung Tsoi, Vladimir Loncar, Sridhara Dasu, Philip Harris   +3 more
doaj   +2 more sources

ISR: Invertible Symbolic Regression [PDF]

CoRR
We introduce an Invertible Symbolic Regression (ISR) method. It is a machine learning technique that generates analytical relationships between inputs and outputs of a given dataset via invertible maps (or architectures). The proposed ISR method naturally combines the principles of Invertible Neural Networks (INNs) and Equation Learner (EQL), a neural ...
Tony Tohme, Mohammad Javad Khojasteh, Mohsen Sadr, Florian Meyer, Kamal Youcef-Toumi   +4 more
core   +5 more sources

Extending a physics-based constitutive model using genetic programming

Applications in Engineering Science, 2022
In material science, models are derived to predict emergent material properties (e.g. elasticity, strength, conductivity) and their relations to processing conditions.
Gabriel Kronberger, Evgeniya Kabliman, Johannes Kronsteiner, Michael Kommenda   +3 more
doaj   +1 more source

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, Ana Helena Kolody, Johannes Kronsteiner, Michael Kommenda, Gabriel Kronberger   +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, Bahareh Etaati, Mehdi Neshat, Seyedali Mirjalili   +3 more
doaj   +1 more source

Priors for symbolic regression

Proceedings of the Companion Conference on Genetic and Evolutionary Computation, 2023
8+2 pages, 2 figures.
Bartlett, D, Desmond, H, Ferreira, P
openaire   +4 more sources

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, Gabriel Kronberger, Ricardo Fernández, Gizo Bokuchava, Patrice Halodova, Alberto Sáez-Maderuelo, Gaspar González-Doncel, J. Ignacio Hidalgo   +7 more
doaj   +1 more source

Continuous improvement and adaptation of predictive models in smart manufacturing and model management

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, Gabriel Kronberger, Michael Affenzeller   +2 more
doaj   +1 more source

Neural Symbolic Regression that Scales

CoRR, 2021
Accepted at the 38th International Conference on Machine Learning (ICML ...
Biggio, Luca, Bendinelli, Tommaso, Neitz, Alexander, Lucchi, Aurelien, Parascandolo, Giambattista   +4 more
openaire   +5 more sources

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, Oldemar Rodríguez   +1 more
doaj   +1 more source