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Topics include measurement uncertainty, propagation of error, diffusion, dispersion, air-water exchange, dissolution, gravity currents, particle transport, and transport in porous media.
The proposed algorithms, based on propagation of error probabilities and shape of erroneous waveforms, are scalable to very large circuits.
When calculating the change in surface elevations, propagation of error produces uncertainties that are twice those of the individual measurements of surface elevation, as noted in Fig. 1b and Supplementary Fig. S4.
To target both optimized and robust conditions a multiple optimization combining the response surface and the propagation of error methods were employed.
The propagation of error technique is built into the design process as an additional response to be optimized via desirability function and hence make the design robust.
In order to achieve the minimal variation in responses, the optimal factor settings were found by minimizing the propagation of error (POE).
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On the basis of a propagation-of-error analysis incorporating the limits of precision for individual experimental parameters, we estimate the accuracy of all CCS values to be better than 2% (see Supporting Information).
Large-scale quantum computations are hampered by the propagation of errors.
The propagation of errors in the hierarchical multi-resolution approach is assessed with Monte Carlos simulations.
Propagation of errors is tracked through the quantification of uncertainties from both inversion and interpolation procedures.
Tight coupling between coefficients enables propagation of errors from high-frequency to low-frequency modes.
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