In line with this observation, we propose a class of adaptive algorithms to find effective sampling strategies that control the length of sampled trajectories.
Based on this observation, we propose adaptive sampling algorithms to determine an appropriate sampling trajectory length to learn an unknown dynamics given the ...
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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.
Feb 1, 2023 · We perform dynamic selection of critical samples and develop an adaptive sampling-learning method for dynamical systems based on the ...
Duration: 29:35
Posted: Aug 30, 2022
Posted: Aug 30, 2022
Jul 18, 2023 · We begin our treatment with an overview of theoretically transparent methods where we discuss principles and guidelines for adaptive sampling.
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 ...
In this work we propose a deep adaptive sampling (DAS) method for solving partial differential equations (PDEs), where deep neural networks are utilized to ...
Missing: dynamical | Show results with:dynamical
We propose a distributed strategy, where robots collect sparse sensor measurements, create a reduced-order model (ROM) of a spatio-temporal process, and use ...
Nov 5, 2021 · In this contribution, we propose a novel methodology to adaptively sample rigorous dynamic process models to generate a dataset for building ...