no code implementations • 28 Sep 2023 • Shaoming Xu, Ankush Khandelwal, Arvind Renganathan, Vipin Kumar
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 distributed (IID) samples and initializes RNNs with zero hidden states.
1 code implementation • 15 Oct 2022 • Shaoming Xu, Ankush Khandelwal, Xiang Li, Xiaowei Jia, Licheng Liu, Jared Willard, Rahul Ghosh, Kelly Cutler, Michael Steinbach, Christopher Duffy, John Nieber, Vipin Kumar
To address this issue, we further propose a new strategy which augments a training segment with an initial value of the target variable from the timestep right before the starting of the training segment.
no code implementations • 2 Dec 2020 • Ankush Khandelwal, Shaoming Xu, Xiang Li, Xiaowei Jia, Michael Stienbach, Christopher Duffy, John Nieber, Vipin Kumar
The goal of this work is to incorporate our understanding of physical processes and constraints in hydrology into machine learning algorithms, and thus bridge the performance gap while reducing the need for large amounts of data compared to traditional data-driven approaches.
no code implementations • 26 Sep 2020 • Xiaowei Jia, Jacob Zwart, Jeffrey Sadler, Alison Appling, Samantha Oliver, Steven Markstrom, Jared Willard, Shaoming Xu, Michael Steinbach, Jordan Read, Vipin Kumar
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.
no code implementations • 10 Mar 2020 • Jared Willard, Xiaowei Jia, Shaoming Xu, Michael Steinbach, Vipin Kumar
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.