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The goal of diffusion models is to learn a diffusion process for a given dataset, such that the process can generate new elements that are distributed similarly as the original dataset. A diffusion model models data as generated by a diffusion process, whereby a new datum performs a random walk with drift through the space of all possible data. [2]
Diffusion in the monolayer: oscillations near temporary equilibrium positions and jumps to the nearest free places. Diffusion of reagents on the surface of a catalyst may play an important role in heterogeneous catalysis. The model of diffusion in the ideal monolayer is based on the jumps of the reagents on the nearest free places.
Self diffusion, exemplified with an isotopic tracer of radioactive isotope 22 Na Example of chemical (classical, Fick's, or Fickian) diffusion of sodium chloride in water. Fundamentally, two types of diffusion are distinguished: Tracer diffusion and Self-diffusion, which is a spontaneous mixing of molecules taking place in the absence of ...
Fick's first law relates the diffusive flux to the gradient of the concentration. It postulates that the flux goes from regions of high concentration to regions of low concentration, with a magnitude that is proportional to the concentration gradient (spatial derivative), or in simplistic terms the concept that a solute will move from a region of high concentration to a region of low ...
A water model is defined by its geometry, together with other parameters such as the atomic charges and Lennard-Jones parameters. In computational chemistry, a water model is used to simulate and thermodynamically calculate water clusters, liquid water, and aqueous solutions with explicit solvent.
In diffusion weighted imaging (DWI), the intensity of each image element reflects the best estimate of the rate of water diffusion at that location.Because the mobility of water is driven by thermal agitation and highly dependent on its cellular environment, the hypothesis behind DWI is that findings may indicate (early) pathologic change.
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In probability theory and statistics, diffusion processes are a class of continuous-time Markov process with almost surely continuous sample paths. Diffusion process is stochastic in nature and hence is used to model many real-life stochastic systems.