Search Results for author: Jimeng Shi

Found 8 papers, 4 papers with code

TimeX++: Learning Time-Series Explanations with Information Bottleneck

1 code implementation15 May 2024 Zichuan Liu, Tianchun Wang, Jimeng Shi, Xu Zheng, Zhuomin Chen, Lei Song, Wenqian Dong, Jayantha Obeysekera, Farhad Shirani, Dongsheng Luo

The design of the objective function builds upon the principle of information bottleneck (IB), and modifies the IB objective function to avoid trivial solutions and distributional shift issues.

Time Series

FIDLAR: Forecast-Informed Deep Learning Architecture for Flood Mitigation

1 code implementation20 Feb 2024 Jimeng Shi, Zeda Yin, Arturo Leon, Jayantha Obeysekera, Giri Narasimhan

FIDLAR seamlessly integrates two neural network modules: one called the Flood Manager, which is responsible for generating water pre-release schedules, and another called the Flood Evaluator, which assesses these generated schedules.

Management Model Predictive Control

Graph Transformer Network for Flood Forecasting with Heterogeneous Covariates

no code implementations11 Oct 2023 Jimeng Shi, Vitalii Stebliankin, Zhaonan Wang, Shaowen Wang, Giri Narasimhan

In this paper, we propose a Flood prediction tool using Graph Transformer Network (FloodGTN) for river systems.


Deep Learning Models for Flood Predictions in South Florida

1 code implementation28 Jun 2023 Jimeng Shi, Zeda Yin, Rukmangadh Myana, Khandker Ishtiaq, Anupama John, Jayantha Obeysekera, Arturo Leon, Giri Narasimhan

To overcome this problem, we train several deep learning (DL) models for use as surrogate models to rapidly predict the water stage.

Time Series Forecasting (TSF) Using Various Deep Learning Models

no code implementations23 Apr 2022 Jimeng Shi, Mahek Jain, Giri Narasimhan

In this paper, we study how the performance of predictive models change as a function of different look-back window sizes and different amounts of time to predict into the future.

Time Series Time Series Forecasting

Computational Simulation and Analysis of Major Control Parameters of Time-Dependent PV/T Collectors

no code implementations1 May 2021 Jimeng Shi, Cheng-Xian Lin

In order to improve performance of photovoltaic/thermal (or PV/T for simplicity) collectors, this paper firstly validated a previous computational thermal model and then introduced an improved computational thermal model to investigate the effects of the major control parameters on the thermal performance of PV/T collectors, including solar cell temperature, back surface temperature, and outlet water temperature.

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