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Diffusion and swelling are modeled by a random walk of mobile cells, and kinetics of chemical or physical processes by probabilities of conversion from one state to another.
Diffusion is modeled by a random walk of mobile cells and kinetics of chemical or physical processes by probabilities of conversion from one state to another.
Conventionally, the FDF is represented by an ensemble of particles and transport is modeled by a random walk in physical space.
In particular, we present efficient algorithms for exactly identifying Boolean threshold functions and 2-term RSE, and for learning 2-term-DNF, when the examples are generated by a random walk on {0,1}n.
To obtain a numerical solution, we develop a grid-less Monte Carlo particle tracking approach, where diffusion is modeled by a random walk of the particles, and reaction is modeled by annihilation of particles.
A bond swap algorithm, resulting from coupling the molecular dynamics and Monte Carlo methods, has been developed to equilibrate initial structures of buckypaper, generated by a random walk approach.
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The adjacency matrix of a graph G = (V, E) is defined by We can learn a lot about the structure of a graph by taking a random walk on it.
For comparison, an analytical model of adsorbate self-diffusivity is constructed by applying a random walk theory to this lattice.
In our method, the predicting problem is formalized by designing a random walk in temporal uncertain networks.
The sound field is estimated from the spatial variation of phonon density which is calculated by using a random walk technique.
Rahmani et al.12 predicted unknown ADRs by applying a random walk algorithm to a network with drug and ADR nodes, where drug-ADR edges represent known ADRs and drug-drug edges indicate drug target similarity, but did not validate new ADRs in any real-world clinical data.
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