We propose a class of adaptive algorithms to find effective sampling strategies that control the length of sampled trajectories.
Through numerical experiments, we show the adaptive algorithms can achieve more accurate results given a sampling budget compared to baseline sampling methods.
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Feb 1, 2023 · We perform dynamic selection of critical samples and develop an adaptive sampling-learning method for dynamical systems based on the ...
Dec 11, 2023 · Adaptive sampling methods are focused on accelerating the sampling of state transitions and the convergence of thermodynamic and kinetic models ...
Missing: dynamical systems.
Jul 18, 2023 · We focus on adaptive sampling algorithms. These techniques differ from other approaches because the thermodynamic ensemble is preserved.
Missing: dynamical | Show results with:dynamical
The active learning/adaptive sampling is used to create a feedback loop between the deep learning model and VRM simulator. This enables faster training ...
Nov 5, 2021 · In this contribution, we propose a novel methodology to adaptively sample rigorous dynamic process models to generate a dataset for building dynamic surrogate ...
Dec 21, 2023 · We begin our treatment with an overview of theoretically transparent methods, where we discuss principles and guidelines for adaptive sampling.
Jul 29, 2021 · Abstract:For economic nonlinear model predictive control and dynamic real-time optimization fast and accurate models are necessary.