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Results for “regression”
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Bayesian multitask learning regression for heterogeneous patient cohorts
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Bayesian linear regression for surface roughness prediction
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Pairwise Difference Regression: A Machine Learning Meta-algorithm for Improved Prediction and Uncertainty Quantification in Chemical Search
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Lattice Thermal Conductivity Prediction Using Symbolic Regression and Machine Learning
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Pursuit of the Ultimate Regression Model for Samarium(III), Europium(III), and LiCl Using Laser-Induced Fluorescence, Design of Experiments, and a Genetic Algorithm for Feature Selection
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Optical Turbulence Profile Modeling in the Atmospheric Boundary Layer: A Random Forest Regression Approach
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A selective small-molecule STAT5 PROTAC degrader capable of achieving tumor regression in vivo
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A compact and accurate semi-global potential energy surface for malonaldehyde from constrained least squares regression
We present a new approach to semi-global potential energy surface fitting that uses the least absolute shrinkage and selection operator (LASSO) constrained least squares procedure to exploit an extremely flexible form for the potential function, while at the same time controlling the risk of overfitting and avoiding the introduction of unphysical features such as divergences or high-frequency oscillations. Drawing from a massively redundant set of overlapping distributed multi-dimensional Gaussian functions of inter-atomic separations we build a compact full-dimensional surface for malonaldehyde, fit to explicitly correlated coupled cluster CCSD(T)(F12*) energies with a root mean square deviations accuracy of 0.3%–0.5% up to 25 000 cm{sup −1} above equilibrium. Importance-sampled diffusion Monte Carlo calculations predict zero point energies for malonaldehyde and its deuterated isotopologue of 14 715.4(2) and 13 997.9(2) cm{sup −1} and hydrogen transfer tunnelling splittings of 21.0(4) and 3.2(4) cm{sup −1}, respectively, which are in excellent agreement with the experimental values of 21.583 and 2.915(4) cm{sup −1}.
Supervised machine learning-based multivariate regression of parallel closures for a high-collisionality deuterium-carbon plasma
Many plasmas of interest in laboratory experiments and space consist of multiple ion species. In tokamak edge plasmas, for instance, ionized impurities expelled from the vessel wall influence plasma transport. When describing multi-species plasmas using fluid equations, we need accurate closure relations to close the set of fluid equations. In this study, we introduce the development of fitting formulas for parallel closures using supervised machine learning, in conjunction with the recent closure theory, considering multi-ion collisions and arbitrary ion temperatures. We apply this approach to a high-collisionality deuterium-carbon plasma and demonstrate its effectiveness. As a result, the machine learning-based method for developing practical and accurate closures can be extended to a wider range of plasmas.
Partial molar properties from molecular simulation using multiple linear regression
Partial molar volumes, energies, and enthalpies can be computed from NpT-Gibbs ensemble simulations through a post-processing procedure that leverages fluctuations in composition, total volume, and total energy of a simulation box. By recording the instantaneous box volumes V and instantaneous number of molecules N i of each of n species for M frames, a large M ×n matrix N is constructed, as well as the M × 1 vector V. The 1 × n vector of partial molar volumes V¯ may then be solved using N • V¯=V. A similar construction permits calculation of partial molar energies using M instantaneous measurements of the total energy of the simulation box, and N • U¯=U. Partial molar enthalpies may be derived from U¯, V¯, and pressure p. These properties may be used to calculate enthalpy and entropy of transfer (absorption, extraction, and adsorption) for species in complex mixtures. The method is demonstrated on three systems in the NpT-Gibbs ensemble: a highly compressible natural gas condensate of methane, n-butane, and n-decane, the liquid-phase adsorption of 1,5-pentanediol and ethanol onto the MFI zeolite, and a relatively incompressible mixture of ethanol, n-dodecane, and water at liquid-liquid equilibrium. Property predictions are compared to those from numerical differentiation of simulation data sequentially changing the composition and from equations of state. Here, the method can also be extended to reaction equilibria in a closed system and is applied to a reactive first-principles Monte Carlo simulation of compressed nitrogen/oxygen.
Robust estimation and outlier detection for varying-coefficient models via penalized regression
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