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The algorithm uses a machine-learning technique known as reinforcement learning.
The algorithm uses a new hierarchical data association method that keeps multiple associations per particle.
The algorithm uses a fixed computational domain with the flow domain immersed in its interior.
The algorithm uses a fixed computational domain with flow domain immersed inside the computational domain.
The algorithm uses a combination of feature extraction, indexing, refinement, and decision processes.
The algorithm uses a Cartesian lattice to divide the parametric domain into adjacent rectangular cells.
The algorithm uses a methodology based on the extension of the state vector.
To do this, the algorithm uses a combination of the mathematics of complicated networks and Boolean algebra to define various regions on a batch of nanotubes.
The algorithm uses a simple strategy for handling data: only two states are maintained for each simulated component.
The algorithm uses a preconditioning matrix that introduces well-conditioned eigenvalues while simultaneously avoiding nonphysical time reversals for viscous flows.
The algorithm uses a semi-discrete wave propagation method to find approximate solution of this model numerically.
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