scholarly journals Learning multivariate functions with low-dimensional structures using polynomial bases

2021 ◽  
pp. 113821
Author(s):  
D. Potts ◽  
M. Schmischke
Keyword(s):  
Multivariate Functions ◽  
Polynomial Bases ◽  
Low Dimensional

scholarly journals Easy representation of multivariate functions with low-dimensional terms via Gaussian process regression kernel design: applications to machine learning of potential energy surfaces and kinetic energy densities from sparse data

2022 ◽  
Author(s):  
Sergei Manzhos ◽  
Eita Sasaki ◽  
Manabu Ihara
Keyword(s):  
Machine Learning ◽  
Kinetic Energy ◽  
Potential Energy ◽  
Gaussian Process ◽  
Potential Energy Surfaces ◽  
Gaussian Process Regression ◽  
Multivariate Functions ◽  
Low Dimensional ◽  
Energy Surfaces ◽  
Kernel Design

Abstract We show that Gaussian process regression (GPR) allows representing multivariate functions with low-dimensional terms via kernel design. When using a kernel built with HDMR (High-dimensional model representation), one obtains a similar type of representation as the previously proposed HDMR-GPR scheme while being faster and simpler to use. We tested the approach on cases where highly accurate machine learning is required from sparse data by fitting potential energy surfaces and kinetic energy densities.

Thermoelectric Properties of PbSr(Se,Te)-Based Low Dimensional Structures

2000 ◽  
Vol 626 ◽  
Author(s):  
Harald Beyer ◽  
Joachim Nurnus ◽  
Harald Böttner ◽  
Armin Lambrecht ◽  
Lothar Schmitt ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Thermoelectric Properties ◽  
Power Factor ◽  
Evaluation Method ◽  
Interband Transition ◽  
Multiple Quantum Well ◽  
Calculated Data ◽  
Multiple Quantum ◽  
Ir Transmission ◽  
Low Dimensional

ABSTRACTThermoelectric properties of low dimensional structures based on PbTe/PbSrTe-multiple quantum-well (MQW)-structures with regard to the structural dimensions, doping profiles and levels are presented. Interband transition energies and barrier band-gap are determined from IR-transmission spectra and compared with Kronig-Penney calculations. The influence of the data evaluation method to obtain the 2D power factor will be discussed. The thermoelectrical data of our layers show a more modest enhancement in the power factor σS2 compared with former publications and are in good agreement with calculated data from Broido et al. [5]. The maximum allowed doping level for modulation doped MQW structures is determined. Thermal conductivity measurements show that a ZT enhancement can be achieved by reducing the thermal conductivity due to interface scattering. Additionally promising lead chalcogenide based superlattices for an increased 3D figure of merit are presented.

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