Zobrazeno 1 - 10
of 252
pro vyhledávání: '"Steinbach, Michael"'
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
Autor:
Chatterjee, Somya Sharma, Lindsay, Kelly, Chatterjee, Neel, Patil, Rohan, De Callafon, Ilkay Altintas, Steinbach, Michael, Giron, Daniel, Nguyen, Mai H., Kumar, Vipin
In recent years, the increasing threat of devastating wildfires has underscored the need for effective prescribed fire management. Process-based computer simulations have traditionally been employed to plan prescribed fires for wildfire prevention. H
Externí odkaz:
http://arxiv.org/abs/2310.01593
Causal DAGs(Directed Acyclic Graphs) are usually considered in a 2D plane. Edges indicate causal effects' directions and imply their corresponding time-passings. Due to the natural restriction of statistical models, effect estimation is usually appro
Externí odkaz:
http://arxiv.org/abs/2211.08573
Autor:
Xu, Shaoming, Khandelwal, Ankush, Li, Xiang, Jia, Xiaowei, Liu, Licheng, Willard, Jared, Ghosh, Rahul, Cutler, Kelly, Steinbach, Michael, Duffy, Christopher, Nieber, John, Kumar, Vipin
In many environmental applications, recurrent neural networks (RNNs) are often used to model physical variables with long temporal dependencies. However, due to mini-batch training, temporal relationships between training segments within the batch (i
Externí odkaz:
http://arxiv.org/abs/2210.08347
Electronic Health Records (EHR) data analysis plays a crucial role in healthcare system quality. Because of its highly complex underlying causality and limited observable nature, causal inference on EHR is quite challenging. Deep Learning (DL) achiev
Externí odkaz:
http://arxiv.org/abs/2011.05466
Autor:
Jia, Xiaowei, Zwart, Jacob, Sadler, Jeffrey, Appling, Alison, Oliver, Samantha, Markstrom, Steven, Willard, Jared, Xu, Shaoming, Steinbach, Michael, Read, Jordan, Kumar, Vipin
This paper proposes a physics-guided machine learning approach that combines advanced machine learning models and physics-based models to improve the prediction of water flow and temperature in river networks. We first build a recurrent graph network
Externí odkaz:
http://arxiv.org/abs/2009.12575
There is a growing consensus that solutions to complex science and engineering problems require novel methodologies that are able to integrate traditional physics-based modeling approaches with state-of-the-art machine learning (ML) techniques. This
Externí odkaz:
http://arxiv.org/abs/2003.04919
Autor:
Jia, Xiaowei, Willard, Jared, Karpatne, Anuj, Read, Jordan S, Zwart, Jacob A, Steinbach, Michael, Kumar, Vipin
Physics-based models of dynamical systems are often used to study engineering and environmental systems. Despite their extensive use, these models have several well-known limitations due to simplified representations of the physical processes being m
Externí odkaz:
http://arxiv.org/abs/2001.11086
Autor:
Jia, Xiaowei, Willard, Jared, Karpatne, Anuj, Read, Jordan, Zwart, Jacob, Steinbach, Michael, Kumar, Vipin
This paper proposes a physics-guided recurrent neural network model (PGRNN) that combines RNNs and physics-based models to leverage their complementary strengths and improve the modeling of physical processes. Specifically, we show that a PGRNN can i
Externí odkaz:
http://arxiv.org/abs/1810.13075
Autor:
Agrawal, Saurabh, Steinbach, Michael, Boley, Daniel, Chatterjee, Snigdhansu, Atluri, Gowtham, Dang, Anh The, Liess, Stefan, Kumar, Vipin
In many domains, there is significant interest in capturing novel relationships between time series that represent activities recorded at different nodes of a highly complex system. In this paper, we introduce multipoles, a novel class of linear rela
Externí odkaz:
http://arxiv.org/abs/1810.02950