Results 21 to 30 of about 150,984 (329)

Revealing Complex Ecological Dynamics via Symbolic Regression. [PDF]

Bioessays, 2019
ABSTRACTComplex ecosystems, from food webs to our gut microbiota, are essential to human life. Understanding the dynamics of those ecosystems can help us better maintain or control them. Yet, reverse-engineering complex ecosystems (i.e., extracting their dynamic models) directly from measured temporal data has not been very successful so far.
Chen Y, Angulo MT, Liu YY.
europepmc   +5 more sources

Generalizability Improvement of Interpretable Symbolic Regression Models for Quantitative Structure–Activity Relationships [PDF]

ACS Omega
Raku Shirasawa, Katsushi Takaki, Tomoyuki Miyao   +2 more
openalex   +2 more sources

Physically interpretable interatomic potentials via symbolic regression and reinforcement learning [PDF]

npj Computational Materials
The development of next-generation molecular simulation models requires moving beyond predefined functional forms toward machine learning (ML) techniques that directly capture multiscale physics.
Bilvin Varughese, Troy D. Loeffler, Suvo Banik, Aditya Koneru, Sukriti Manna, Karthik Balasubramanian, Rohit Batra, Mathew J. Cherukara, Orcun Yildiz, Tom Peterka, Bobby G. Sumpter, Subramanian K.R.S. Sankaranarayanan   +11 more
doaj   +2 more sources

Function Space Optimization: A symbolic regression method for estimating parameter transfer functions for hydrological models [PDF]

Water Resour Res, 2020
Moritz Feigl, Mathew Herrnegger, Daniel Klotz, Karsten Schulz   +3 more
openalex   +2 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

Symbolic-regression boosting

Genetic Programming and Evolvable Machines, 2021
Modifying standard gradient boosting by replacing the embedded weak learner in favor of a strong(er) one, we present SyRBo: Symbolic-Regression Boosting. Experiments over 98 regression datasets show that by adding a small number of boosting stages -- between 2--5 -- to a symbolic regressor, statistically significant improvements can often be attained ...
Moshe Sipper, Jason H. Moore
openaire   +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, 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

A Review on Symbolic Regression in Power Systems: Methods, Applications, and Future Directions [PDF]

Renewable and Sustainable Energy Reviews
Amir Bahador Javadi, Philip W. T. Pong
openalex   +3 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