4 days ago · Traditional physics-based models are first-principled, explainable, and sample-efficient. However, they often rely on strong modeling assumptions and expensive ...

6 days ago · Abstract. This study presents an innovative modeling approach for systems with uncertain dynamics, utilizing a novel long-short-term memory (LSTM) architecture ...

6 days ago · It dynamically balances the development and exploration phases of evolution by adapting to different search stages.

1 day ago · To address this, we introduce SubLIME, a data-efficient evaluation framework that employs adaptive sampling techniques, such as clustering and quality-based ...

6 days ago · Finally, the Adaptive Strategy adjusts the sampling probabilities based on the incoming information, ensuring dynamic adaptation to changing data conditions.

1 day ago · We show that this methodology can be used to learn gain matrices for filtering linear and nonlinear dynamical systems, as well as inflation and localization ...

7 days ago · To tackle such a problem, we design an adaptive energy-based sequential method and introduce the energy dissipation evolution property to adaptively control ...

3 days ago · Active learning for estimating reachable sets for systems with unknown dynamics. ... adaptive sampling Kriging-based method. Comput. Chem. Eng. 2012, 36, 358 ...

5 hours ago · In this paper, a novel adaptive sampling strategy is introduced for these models that uses field-based uncertainty as a sampling metric. The strategy uses Monte ...

2 days ago · We introduce a framework and methodology for learning parameterized filters and apply it to learning gains for fil- tering linear and nonlinear dynamical ...