Generalised Latent Assimilation in Heterogeneous Reduced Spaces with Machine Learning Surrogate Models

EnKF data-driven reduced order assimilation system

(2022) C. Liu et al.   ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS

Data-Enabled Physics-Informed Machine Learning for Reduced-Order Modeling Digital Twin: Application to Nuclear Reactor Physics

(2022) Helin Gong et al.   NUCLEAR SCIENCE AND ENGINEERING

Data-driven surrogate model with latent data assimilation: Application to wildfire forecasting

(2022) Sibo Cheng et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Deep Data Assimilation: Integrating Deep Learning with Data Assimilation

(2021) Rossella Arcucci et al.   Applied Sciences-Basel

Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow

(2021) Taichi Nakamura et al.   PHYSICS OF FLUIDS

A deep learning-based method for grip strength prediction: Comparison of multilayer perceptron and polynomial regression approaches

(2021) Jaejin Hwang et al.   PLoS One

An autoencoder‐based reduced‐order model for eigenvalue problems with application to neutron diffusion

(2021) Toby R. F. Phillips et al.   INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING

A convolutional strategy on unstructured mesh for the adjoint vector modeling

(2021) Mengfei Xu et al.   PHYSICS OF FLUIDS

Latent Space Data Assimilation by using Deep Learning

(2021) Mathis Peyron et al.   QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY

Using machine learning to correct model error in data assimilation and forecast applications

(2021) Alban Farchi et al.   QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY

Deep Neural Network and Polynomial Chaos Expansion-Based Surrogate Models for Sensitivity and Uncertainty Propagation: An Application to a Rockfill Dam

(2021) Gullnaz Shahzadi et al.   Water

Observation data compression for variational assimilation of dynamical systems

(2021) Sibo Cheng et al.   Journal of Computational Science

An inverse-distance-based fitting term for 3D-Var data assimilation in nuclear core simulation

(2020) Helin Gong et al.   ANNALS OF NUCLEAR ENERGY

Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: A case study with the Lorenz 96 model

(2020) Julien Brajard et al.   Journal of Computational Science

Error covariance tuning in variational data assimilation: application to an operating hydrological model

(2020) Sibo Cheng et al.   STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT

Data-driven polynomial chaos expansion for machine learning regression

(2019) Emiliano Torre et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Learning ReLU Networks on Linearly Separable Data: Algorithm, Optimality, and Generalization

(2019) Gang Wang et al.   IEEE TRANSACTIONS ON SIGNAL PROCESSING

An artificial neural network framework for reduced order modeling of transient flows

(2019) Omer San et al.   Communications in Nonlinear Science and Numerical Simulation

Nonlinear mode decomposition with convolutional neural networks for fluid dynamics

(2019) Takaaki Murata et al.   JOURNAL OF FLUID MECHANICS

Background error covariance iterative updating with invariant observation measures for data assimilation

(2019) Sibo Cheng et al.   STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT

Data assimilation in the geosciences: An overview of methods, issues, and perspectives

(2018) Alberto Carrassi et al.   Wiley Interdisciplinary Reviews-Climate Change

Optimal reduced space for Variational Data Assimilation

(2018) Rossella Arcucci et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Optical measurements in evolving dispersed pipe flows

(2017) Victor Voulgaropoulos et al.   EXPERIMENTS IN FLUIDS

On the variational data assimilation problem solving and sensitivity analysis

(2017) Rossella Arcucci et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review

(2017) Waseem Rawat et al.   NEURAL COMPUTATION

On the interaction of observation and prior error correlations in data assimilation

(2017) A. M. Fowler et al.   QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY

Local Prediction of Chaotic Time Series Based on Polynomial Coefficient Autoregressive Model

(2015) Liyun Su et al.   MATHEMATICAL PROBLEMS IN ENGINEERING

Prediction of multivariate chaotic time series with local polynomial fitting

(2009) Li-yun Su   COMPUTERS & MATHEMATICS WITH APPLICATIONS