Residual-based Adaptive Refinement is a strategy used to improve the accuracy and efficiency of by intelligently selecting training data points.
: It significantly improves the speed at which a model converges to a solution.
: Traditional RAR does not differentiate between points if they all have "large" residuals, which can lead to less optimal point selection compared to more modern active-learning-based ranking methods. arXiv:2112.13988v1 [math.NA] 28 Dec 2021
: The method identifies "large residual error points"—areas where the model's current predictions deviate most from the physical laws it is trying to learn. It then adds more training points in those specific regions to refine the model's accuracy. Comparison to Other Methods :
13988 Rar 🚀
Residual-based Adaptive Refinement is a strategy used to improve the accuracy and efficiency of by intelligently selecting training data points.
: It significantly improves the speed at which a model converges to a solution. 13988 rar
: Traditional RAR does not differentiate between points if they all have "large" residuals, which can lead to less optimal point selection compared to more modern active-learning-based ranking methods. arXiv:2112.13988v1 [math.NA] 28 Dec 2021 Residual-based Adaptive Refinement is a strategy used to
: The method identifies "large residual error points"—areas where the model's current predictions deviate most from the physical laws it is trying to learn. It then adds more training points in those specific regions to refine the model's accuracy. Comparison to Other Methods : 13988 rar