In this work,we have proposed a generative model,called VAE-KRnet,for density estimation or approximation,which combines the canonical variational autoencoder(VAE)with our recently developed flow-based generativemodel...
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In this work,we have proposed a generative model,called VAE-KRnet,for density estimation or approximation,which combines the canonical variational autoencoder(VAE)with our recently developed flow-based generativemodel,called *** is used as a dimension reduction technique to capture the latent space,and KRnet is used to model the distribution of the latent *** a linear model between the data and the latent variable,we show that VAE-KRnet can be more effective and robust than the canonical ***-KRnet can be used as a density model to approximate either data distribution or an arbitrary probability density function(PDF)known up to a ***-KRnet is flexible in terms of *** the number of dimensions is relatively small,KRnet can effectively approximate the distribution in terms of the original random *** high-dimensional cases,we may use VAE-KRnet to incorporate dimension *** important application of VAE-KRnet is the variational Bayes for the approximation of the posterior *** variational Bayes approaches are usually based on the minimization of the Kullback-Leibler(KL)divergence between the model and the *** highdimensional distributions,it is very challenging to construct an accurate densitymodel due to the curse of dimensionality,where extra assumptions are often introduced for *** instance,the classical mean-field approach assumes mutual independence between dimensions,which often yields an underestimated variance due to *** alleviate this issue,we include into the loss the maximization of the mutual information between the latent random variable and the original random variable,which helps keep more information from the region of low density such that the estimation of variance is *** experiments have been presented to demonstrate the effectiveness of our model.
Thermal energy management in metal-organic frameworks(MOFs)is an important,yet often neglected,challenge for many adsorption-based applications such as gas storage and *** its importance,there is insufficient understa...
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Thermal energy management in metal-organic frameworks(MOFs)is an important,yet often neglected,challenge for many adsorption-based applications such as gas storage and *** its importance,there is insufficient understanding of the structure-property relationships governing thermal transport in *** provide a data-driven perspective into these relationships,here we perform large-scale computational screening of thermal conductivity k in MOFs,leveraging classical molecular dynamics simulations and 10,194 hypothetical MOFs created using the ToBaCCo 3.0 *** found that high thermal conductivity in MOFs is favored by high densities(>1.0 g cm^(−3)),small pores(<10Å),and four-connected metal *** also found that 36 MOFs exhibit ultra-low thermal conductivity(<0.02 W m^(−1) K^(−1)),which is primarily due to having extremely large pores(~65Å).Furthermore,we discovered six hypothetical MOFs with very high thermal conductivity(>10 W m^(−1) K^(−1)),the structures of which we describe in additional detail.
Three dimensional free-decaying MHD turbulence is simulated by lattice Boltzmann methods on a spatial grid of 80003 for low and high magnetic Prandtl *** is verified that∇·B=0 is automatically maintained to machi...
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Three dimensional free-decaying MHD turbulence is simulated by lattice Boltzmann methods on a spatial grid of 80003 for low and high magnetic Prandtl *** is verified that∇·B=0 is automatically maintained to machine accuracy throughout the *** of vorticity and current show the persistence of many large scale structures(both magnetic and velocity)for long times—unlike the velocity isosurfaces of Navier-Stokes turbulence.
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