Results 121 to 130 of about 4,489 (189)

Hybrid models combining neural networks (NN), Gaussian process regressions (GPR), and high-dimensional model representations (HDMR) for more powerful machine learning

Hybrid models combining neural networks (NN), Gaussian process regressions (GPR), and high-dimensional model representations (HDMR) for more powerful machine learning
identifier:oai:t2r2.star.titech.ac.jp ...
openaire   +1 more source

Hybrid modeling and forecasting of COVID-19: integrating SEAIQHRD and GPR for improved predictions. [PDF]

BMC Infect Dis
Rao MA, Jaradat EK, Devi MP, Dhandapani PB, Nalule RM, Al-Hmoud M.   +5 more
europepmc   +1 more source

Machine learning-based Direct Normal Irradiance (DNI) forecasting using satellite data for Concentrated Solar Power (CSP) plants with Thermal Energy Storage (TES). [PDF]

Sci Rep
Rathnayake N, Wijewardane S.
europepmc   +1 more source

Modeling the Friction Behavior of Low-Carbon Steel Sheets Using Various Machine Learning Algorithms Based on Strip Drawing Test Data. [PDF]

Materials (Basel)
Trzepieciński T.
europepmc   +1 more source

A novel hybrid CCD-ML approach for predicting permeability alterations in carbonate reservoir rocks during waterflooding under scale inhibitor treatment. [PDF]

Sci Rep
Ahmadi S, Khormali A, Kazemzadeh Y, Dehghani MR.   +3 more
europepmc   +1 more source

Identifying Polymers that Bind or Reject Proteins with Machine Learning: Handling Categorical Features within a GPR Model. [PDF]

ACS Polym Au
De Silva R, Ge W, Bapp C, Mustafa AZ, Chapman R, Fan Y, Sisson SA, Stenzel MH.   +7 more
europepmc   +1 more source

What is the Diatomic Molecule with the Largest Dipole Moment? [PDF]

ACS Omega
Elhalawani A, Shi R, Londoño M, Tomza M, Pérez-Ríos J.   +4 more
europepmc   +1 more source

Malus's law-enhanced Michelson interferometer with PSO-based calibration and data-driven error compensation for medical micro-displacement measurement. [PDF]

Front Artif Intell
Wang Y, Lv Y, Xie L, Dong S.
europepmc   +1 more source

Bayesian Optimization of Catalysis with In-Context Learning. [PDF]

ACS Cent Sci
Caldas Ramos M, Michtavy SS, White AD, Porosoff MD.   +3 more
europepmc   +1 more source

Development of machine learning models for predicting properties of carbon materials and design of process conditions for production of materials with desired multiple properties. [PDF]

Anal Sci
Matsubara M, Sasaki R, Takahara JP, Moritake S, Harada Y, Kaneko H.   +5 more
europepmc   +1 more source