Inverse design of materials by machine learning

J Wang, Y Wang, Y Chen - Materials, 2022 - mdpi.com
It is safe to say that every invention that has changed the world has depended on materials.
At present, the demand for the development of materials and the invention or design of new …

Hybridizing salp swarm algorithm with particle swarm optimization algorithm for recent optimization functions

N Singh, SB Singh, EH Houssein - Evolutionary Intelligence, 2022 - Springer
The salp swarm algorithm (SSA) has shown its fast search speed in several challenging
problems. Research shows that not every nature-inspired approach is suitable for all …

Costs and consequences of chronic pain due to musculoskeletal disorders from a health system perspective in Chile

C Vargas, N Bilbeny, C Balmaceda, MF Rodríguez… - Pain …, 2018 - journals.lww.com
Background: Chronic pain is a prevalent and distressing condition caused by an unceasing
pain lasting more than 3 months or a pain that persists beyond the normal healing time …

Accurate discharge coefficient prediction of streamlined weirs by coupling linear regression and deep convolutional gated recurrent unit

W Chen, D Sharifrazi, G Liang, SS Band… - Engineering …, 2022 - Taylor & Francis
Streamlined weirs, which are a nature-inspired type of weir, have gained tremendous
attention among hydraulic engineers, mainly owing to their established performance with …

On the performance improvement of elephant herding optimization algorithm

MA Elhosseini, RA El Sehiemy, YI Rashwan… - Knowledge-Based …, 2019 - Elsevier
Thanks to fewer numbers of control parameters and easier implementation, the Elephant
Herding Optimization (EHO) has been gaining research interest during the past decade. In …

An improved elephant herding optimization using sine–cosine mechanism and opposition based learning for global optimization problems

H Muthusamy, S Ravindran, S Yaacob… - Expert Systems with …, 2021 - Elsevier
An improved elephant herding optimization (EHOI) is proposed for continuous function
optimization, financial stress prediction problem and two engineering optimization problems …

Energy consumption prediction using machine learning; a review

A Mosavi, A Bahmani - 2019 - preprints.org
Abstract Machine learning (ML) methods has recently contributed very well in the
advancement of the prediction models used for energy consumption. Such models highly …

Reviewing the novel machine learning tools for materials design

A Mosavi, T Rabczuk, AR Varkonyi-Koczy - Recent Advances in …, 2018 - Springer
Computational materials design is a rapidly evolving field of challenges and opportunities
aiming at development and application of multi-scale methods to simulate, predict and select …

Integration of machine learning and optimization for robot learning

A Mosavi, AR Varkonyi-Koczy - … of the 15th international conference on …, 2017 - Springer
Learning ability in Robotics is acknowledged as one of the major challenges facing artificial
intelligence. Although in the numerous areas within Robotics machine learning (ML) has …

Neural networks for modeling and control of particle accelerators

AL Edelen, SG Biedron, BE Chase… - … on Nuclear Science, 2016 - ieeexplore.ieee.org
Particle accelerators are host to myriad nonlinear and complex physical phenomena. They
often involve a multitude of interacting systems, are subject to tight performance demands …