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Found 14 papers, 1 papers with code
A 3D super-resolution of wind fields via physics-informed pixel-wise self-attention generative adversarial network
To mitigate global warming, greenhouse gas sources need to be resolved at a high spatial resolution and monitored in time to ensure the reduction and ultimately elimination of the pollution source.
A Supervised Contrastive Learning Pretrain-Finetune Approach for Time Series
Foundation models have recently gained attention within the field of machine learning thanks to its efficiency in broad data processing.
An End-to-End Time Series Model for Simultaneous Imputation and Forecast
Time series forecasting using historical data has been an interesting and challenging topic, especially when the data is corrupted by missing values.
Inverse Models for Estimating the Initial Condition of Spatio-Temporal Advection-Diffusion Processes
The irregular sampling scheme is the general scenario, while computationally efficient solutions are available in the spectral domain for non-uniform and shifted uniform sampling.
Multi-task Learning for Source Attribution and Field Reconstruction for Methane Monitoring
Inferring the source information of greenhouse gases, such as methane, from spatially sparse sensor observations is an essential element in mitigating climate change.
Super Resolution for Turbulent Flows in 2D: Stabilized Physics Informed Neural Networks
In addition, to compensate for decrease of observer's performance due to the presence of unknown destabilizing forcing, the network is designed to estimate the contribution of the unknown forcing implicitly from the data over the course of training.
S3RP: Self-Supervised Super-Resolution and Prediction for Advection-Diffusion Process
We present a super-resolution model for an advection-diffusion process with limited information.
Generative Adversarial Network for Probabilistic Forecast of Random Dynamical System
We present a deep learning model for data-driven simulations of random dynamical systems without a distributional assumption.
Variational inference formulation for a model-free simulation of a dynamical system with unknown parameters by a recurrent neural network
Unlike the classical variational inference, where a factorized distribution is used to approximate the posterior, we employ a feedforward neural network supplemented by an encoder recurrent neural network to develop a more flexible probabilistic model.
Data-driven Reconstruction of Nonlinear Dynamics from Sparse Observation
It is shown that the fixed-point ESN is able to reconstruct the complex dynamics from only 5 ~ 10% of the data.
Short note on the behavior of recurrent neural network for noisy dynamical system
The behavior of recurrent neural network for the data-driven simulation of noisy dynamical systems is studied by training a set of Long Short-Term Memory Networks (LSTM) on the Mackey-Glass time series with a wide range of noise level.
Deep learning algorithm for data-driven simulation of noisy dynamical system
It is shown that, when the numerical discretization is used, the function estimation problem can be solved by a multi-label classification problem.
Learning temporal evolution of probability distribution with Recurrent Neural Network
We propose to tackle a time series regression problem by computing temporal evolution of a probability density function to provide a probabilistic forecast.
Model-free prediction of noisy chaotic time series by deep learning
We present a deep neural network for a model-free prediction of a chaotic dynamical system from noisy observations.
