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pro vyhledávání: '"Roy, Tirthankar"'
Hydrological models often involve constitutive laws that may not be optimal in every application. We propose to replace such laws with the Kolmogorov-Arnold networks (KANs), a class of neural networks designed to identify symbolic expressions. We dem
Externí odkaz:
http://arxiv.org/abs/2410.11587
In this study, we discuss how reinforcement learning (RL) provides an effective and efficient framework for solving sociohydrology problems. The efficacy of RL for these types of problems is evident because of its ability to update policies in an ite
Externí odkaz:
http://arxiv.org/abs/2405.20772
Autor:
Pokharel, Sudan, Roy, Tirthankar
Significant strides have been made in advancing streamflow predictions, notably with the introduction of cutting-edge machine-learning models. Predominantly, Long Short-Term Memories (LSTMs) and Convolution Neural Networks (CNNs) have been widely emp
Externí odkaz:
http://arxiv.org/abs/2404.07924