Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review
Data assimilation (DA) and uncertainty quantification (UQ) are extensively used in analysing
and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical …
and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical …
Prognostics and health management of rotating machinery of industrial robot with deep learning applications—A review
The availability of computational power in the domain of Prognostics and Health
Management (PHM) with deep learning (DL) applications has attracted researchers …
Management (PHM) with deep learning (DL) applications has attracted researchers …
Generalised latent assimilation in heterogeneous reduced spaces with machine learning surrogate models
Reduced-order modelling and low-dimensional surrogate models generated using machine
learning algorithms have been widely applied in high-dimensional dynamical systems to …
learning algorithms have been widely applied in high-dimensional dynamical systems to …
Parameter flexible wildfire prediction using machine learning techniques: Forward and inverse modelling
Parameter identification for wildfire forecasting models often relies on case-by-case tuning
or posterior diagnosis/analysis, which can be computationally expensive due to the …
or posterior diagnosis/analysis, which can be computationally expensive due to the …
Deep generative data assimilation in multimodal setting
Robust integration of physical knowledge and data is key to improve computational
simulations such as Earth system models. Data assimilation is crucial for achieving this goal …
simulations such as Earth system models. Data assimilation is crucial for achieving this goal …
An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics
This paper proposes an approach that combines reduced-order models with machine
learning in order to create an digital twin to predict the power distribution over the core …
learning in order to create an digital twin to predict the power distribution over the core …
Multi-domain encoder–decoder neural networks for latent data assimilation in dynamical systems
High-dimensional dynamical systems often require computationally intensive physics-based
simulations, making full physical space data assimilation impractical. Latent data …
simulations, making full physical space data assimilation impractical. Latent data …
Integrating recurrent neural networks with data assimilation for scalable data‐driven state estimation
Data assimilation (DA) is integrated with machine learning in order to perform entirely data‐
driven online state estimation. To achieve this, recurrent neural networks (RNNs) are …
driven online state estimation. To achieve this, recurrent neural networks (RNNs) are …
[HTML][HTML] Efficient deep data assimilation with sparse observations and time-varying sensors
Abstract Variational Data Assimilation (DA) has been broadly used in engineering problems
for field reconstruction and prediction by performing a weighted combination of multiple …
for field reconstruction and prediction by performing a weighted combination of multiple …
Towards an end-to-end artificial intelligence driven global weather forecasting system
The weather forecasting system is important for science and society, and significant
achievements have been made in applying artificial intelligence (AI) to medium-range …
achievements have been made in applying artificial intelligence (AI) to medium-range …