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06/01/2023
An End-to-End Time Series Model for Simultaneous Imputation and Forecast
Time series forecasting using historical data has been an interesting an...
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02/08/2023
Inverse Models for Estimating the Initial Condition of Spatio-Temporal Advection-Diffusion Processes
Inverse problems involve making inference about unknown parameters of a ...
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11/02/2022
Multi-task Learning for Source Attribution and Field Reconstruction for Methane Monitoring
Inferring the source information of greenhouse gases, such as methane, f...
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04/15/2022
Super Resolution for Turbulent Flows in 2D: Stabilized Physics Informed Neural Networks
We propose a new design of a neural network for solving a zero shot supe...
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11/08/2021
S3RP: Self-Supervised Super-Resolution and Prediction for Advection-Diffusion Process
We present a super-resolution model for an advection-diffusion process w...
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11/04/2021
Generative Adversarial Network for Probabilistic Forecast of Random Dynamical System
We present a deep learning model for data-driven simulations of random d...
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03/02/2020
Variational inference formulation for a model-free simulation of a dynamical system with unknown parameters by a recurrent neural network
We propose a recurrent neural network for a "model-free" simulation of a...
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10/23/2019
Statistical Modeling for Spatio-Temporal Data from Physical Convection-Diffusion Processes
This paper proposes a statistical modeling approach for spatio-temporal ...
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06/10/2019
Data-driven Reconstruction of Nonlinear Dynamics from Sparse Observation
We present a data-driven model to reconstruct nonlinear dynamics from a ...
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04/05/2019
Short note on the behavior of recurrent neural network for noisy dynamical system
The behavior of recurrent neural network for the data-driven simulation ...
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02/22/2018
Deep learning algorithm for data-driven simulation of noisy dynamical system
We present a deep learning model, DE-LSTM, for the simulation of a stoch...
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01/09/2018