Results 61 to 70 of about 13,019 (261)

Accelerating graph-based tracking tasks with symbolic regression

Machine Learning: Science and Technology
The reconstruction of particle tracks from hits in tracking detectors is a computationally intensive task due to the large combinatorics of detector signals.
Nathalie Soybelman, Carlo Schiavi, Francesco A Di Bello, Eilam Gross   +3 more
doaj   +1 more source

Symbolic Regression for State Estimation of Lithium-Ion Battery

IEEE Access
Modeling lithium-ion batteries has been a challenging problem. One of the critical tasks among many is state estimation, as it enables researchers to design better battery management systems (BMS).
Anubhav Kamal, Shubham Sambhaji Patil, Sagar Bharathraj, Ankur Deshwal, Shashishekar P. Adiga   +4 more
doaj   +1 more source

Karl Popper and the Mechanisms of Hydrogen Embrittlement

Advanced Engineering Materials, EarlyView.
Representation of the beginning of loss of ductility rather than embrittlement. Small concentrations of hydrogen in a diffusible form within iron are well‐established to harm the mechanical integrity of steels. There are theories that attempt to explain the pernicious role of hydrogen.
H. K. D. H. Bhadeshia
wiley   +1 more source

Symbolic Modeling for financial asset pricing

Journal of Finance and Data Science
Symbolic Regression is a machine learning technique that discovers an unknown function from its samples. Compared to conventional regression techniques (e.g., linear regression, polynomial regression, etc.), Symbolic Regression does not limit the ...
Xiangwu Zuo, Anxiao (Andrew) Jiang
doaj   +1 more source

ISR: Invertible Symbolic Regression

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
openaire   +2 more sources

Fostering Innovation: Streamlining Magnetocaloric Materials Research by Digitalization

Advanced Engineering Materials, EarlyView.
Magnetocaloric cooling (MCE) is an environmentally friendly refrigeration method with great potential. Optimizing MCE materials involves the preparation and screening of large quantities of samples, which in turn generates a large amount of data. A digitalization approach is presented that uses ontologies, knowledge graphs, and digital workflows to ...
Simon Bekemeier, Moritz Blum, Luana Caron, Alisa Chirkova, Philipp Cimiano, Basil Ell, Inga Ennen, Michael Feige, Maik Gaerner, Thomas Hilbig, Andreas Hütten, Günter Reiss, Tapas Samanta, Sonja Schöning, Christian Schröder, Lennart Schwan, Chris Taake, Martin Wortmann   +17 more
wiley   +1 more source

A New Approximation for the Perimeter of an Ellipse

Algorithms
We consider the problem of approximating the perimeter of an ellipse, for which there is no known finite formula, in the context of high-precision performance.
Pablo Moscato, Andrew Ciezak
doaj   +1 more source

Symbolic Regression and Multi‐Objective Optimization of the Flory–Huggins Interaction Parameter for Hydrogels

Advanced Engineering Materials, EarlyView.
We develop a data‐driven method to derive the mathematical expressions of the Flory–Huggins interaction parameter χ for the swelling behavior of temperature–responsive hydrogels. Starting from initial assumptions of χ, our workflow combines Bayesian optimization, Flory–Rehner theory, and symbolic regression to generate candidate χ expressions.
Yawen Wang, Adrian Ehrenhofer, Thomas Wallmersperger   +2 more
wiley   +1 more source

Microstructure Evolution of a VMnFeCoNi High‐Entropy Alloy After Synthesis, Swaging, and Annealing

Advanced Engineering Materials, EarlyView.
The synthesis and processing (rotary swaging and annealing) of the novel VMnFeCoNi alloy is investigated, alongside the estimation of the grain size effect on hardness. Analysis of a wide grain size range of recrystallized microstructures (12–210 µm) reveals a low annealing twin density.
Aditya Srinivasan Tirunilai, Zeki Ayrildi, Buse Şen, Maik Rajkowski, Mathias Redondo Garcia, Markus Stricker, Guillaume Laplanche   +6 more
wiley   +1 more source

Forecasting Selected Commodities’ Prices with the Bayesian Symbolic Regression

International Journal of Financial Studies
This study firstly applied a Bayesian symbolic regression (BSR) to the forecasting of numerous commodities’ prices (spot-based ones). Moreover, some features and an initial specification of the parameters of the BSR were analysed.
Krzysztof Drachal, Michał Pawłowski
doaj   +1 more source