Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Renganathan, Arvind"'
Autor:
Xu, Shaoming, Renganathan, Arvind, Khandelwal, Ankush, Ghosh, Rahul, Li, Xiang, Liu, Licheng, Tayal, Kshitij, Harrington, Peter, Jia, Xiaowei, Jin, Zhenong, Nieber, Jonh, Kumar, Vipin
Streamflow, vital for water resource management, is governed by complex hydrological systems involving intermediate processes driven by meteorological forces. While deep learning models have achieved state-of-the-art results of streamflow prediction,
Externí odkaz:
http://arxiv.org/abs/2410.14137
Accurate long-term predictions are the foundations for many machine learning applications and decision-making processes. Traditional time series approaches for prediction often focus on either autoregressive modeling, which relies solely on past obse
Externí odkaz:
http://arxiv.org/abs/2410.12184
Hierarchically Disentangled Recurrent Network for Factorizing System Dynamics of Multi-scale Systems
Autor:
Ghosh, Rahul, McEachran, Zac, Renganathan, Arvind, Lindsay, Kelly, Sharma, Somya, Steinbach, Michael, Nieber, John, Duffy, Christopher, Kumar, Vipin
We present a knowledge-guided machine learning (KGML) framework for modeling multi-scale processes, and study its performance in the context of streamflow forecasting in hydrology. Specifically, we propose a novel hierarchical recurrent neural archit
Externí odkaz:
http://arxiv.org/abs/2407.20152
We introduce TAM-RL (Task Aware Modulation using Representation Learning), a novel multimodal meta-learning framework for few-shot learning in heterogeneous systems, designed for science and engineering problems where entities share a common underlyi
Externí odkaz:
http://arxiv.org/abs/2310.04727
Autor:
Chatterjee, Somya Sharma, Ghosh, Rahul, Renganathan, Arvind, Li, Xiang, Chatterjee, Snigdhansu, Nieber, John, Duffy, Christopher, Kumar, Vipin
In hydrology, modeling streamflow remains a challenging task due to the limited availability of basin characteristics information such as soil geology and geomorphology. These characteristics may be noisy due to measurement errors or may be missing a
Externí odkaz:
http://arxiv.org/abs/2310.02193
Time series modeling, a crucial area in science, often encounters challenges when training Machine Learning (ML) models like Recurrent Neural Networks (RNNs) using the conventional mini-batch training strategy that assumes independent and identically
Externí odkaz:
http://arxiv.org/abs/2309.16882
Accurate long-term predictions are the foundations for many machine learning applications and decision-making processes. However, building accurate long-term prediction models remains challenging due to the limitations of existing temporal models lik
Externí odkaz:
http://arxiv.org/abs/2309.10291
Autor:
Ghosh, Rahul, Yang, Haoyu, Khandelwal, Ankush, He, Erhu, Renganathan, Arvind, Sharma, Somya, Jia, Xiaowei, Kumar, Vipin
Personalized prediction of responses for individual entities caused by external drivers is vital across many disciplines. Recent machine learning (ML) advances have led to new state-of-the-art response prediction models. Models built at a population
Externí odkaz:
http://arxiv.org/abs/2302.08406
Autor:
Sharma, Somya, Ghosh, Rahul, Renganathan, Arvind, Li, Xiang, Chatterjee, Snigdhansu, Nieber, John, Duffy, Christopher, Kumar, Vipin
The astounding success of these methods has made it imperative to obtain more explainable and trustworthy estimates from these models. In hydrology, basin characteristics can be noisy or missing, impacting streamflow prediction. For solving inverse p
Externí odkaz:
http://arxiv.org/abs/2210.06213
Autor:
Ghosh, Rahul, Renganathan, Arvind, Tayal, Kshitij, Li, Xiang, Khandelwal, Ankush, Jia, Xiaowei, Duffy, Chris, Neiber, John, Kumar, Vipin
Machine Learning is beginning to provide state-of-the-art performance in a range of environmental applications such as streamflow prediction in a hydrologic basin. However, building accurate broad-scale models for streamflow remains challenging in pr
Externí odkaz:
http://arxiv.org/abs/2109.06429