Parallelized Monitoring of Dependent Spatiotemporal Processes

2021 ◽  
pp. 165-183
Author(s):  
Philipp Otto
Keyword(s):  
Spatiotemporal Processes

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

2021 ◽  
Author(s):  
Alban Farchi ◽  
Patrick Laloyaux ◽  
Massimo Bonavita ◽  
Marc Bocquet
Keyword(s):  
Machine Learning ◽  
Data Assimilation ◽  
Data Science ◽  
Weather Prediction ◽  
Realistic Model ◽  
Model Error ◽  
Underlying Assumption ◽  
Correct Model ◽  
Recent Developments ◽  
Spatiotemporal Processes

<p>Recent developments in machine learning (ML) have demonstrated impressive skills in reproducing complex spatiotemporal processes. However, contrary to data assimilation (DA), the underlying assumption behind ML methods is that the system is fully observed and without noise, which is rarely the case in numerical weather prediction. In order to circumvent this issue, it is possible to embed the ML problem into a DA formalism characterised by a cost function similar to that of the weak-constraint 4D-Var (Bocquet et al., 2019; Bocquet et al., 2020). In practice ML and DA are combined to solve the problem: DA is used to estimate the state of the system while ML is used to estimate the full model. </p><p>In realistic systems, the model dynamics can be very complex and it may not be possible to reconstruct it from scratch. An alternative could be to learn the model error of an already existent model using the same approach combining DA and ML. In this presentation, we test the feasibility of this method using a quasi geostrophic (QG) model. After a brief description of the QG model model, we introduce a realistic model error to be learnt. We then asses the potential of ML methods to reconstruct this model error, first with perfect (full and noiseless) observation and then with sparse and noisy observations. We show in either case to what extent the trained ML models correct the mid-term forecasts. Finally, we show how the trained ML models can be used in a DA system and to what extent they correct the analysis.</p><p>Bocquet, M., Brajard, J., Carrassi, A., and Bertino, L.: Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models, Nonlin. Processes Geophys., 26, 143–162, 2019</p><p>Bocquet, M., Brajard, J., Carrassi, A., and Bertino, L.: Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization, Foundations of Data Science, 2 (1), 55-80, 2020</p><p>Farchi, A., Laloyaux, P., Bonavita, M., and Bocquet, M.: Using machine learning to correct model error in data assimilation and forecast applications, arxiv:2010.12605, submitted. </p>

Development of Spatiotemporal Recurrent Neural Network for Modeling of Spatiotemporal Processes

2021 ◽  
Vol 17 (1) ◽  
pp. 189-198
Author(s):  
Xinjiang Lu ◽  
Du Xu ◽  
Wenbo Liu
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Spatiotemporal Processes

Sodium ion channel optical model: Depolarization spatial distribution and local potential dynamic spatiotemporal processes

2013 ◽  
Vol 99 ◽  
pp. 261-269 ◽  
Author(s):  
Fusheng Zha ◽  
Mantian Li ◽  
Wei Guo ◽  
Jiaxuan Chen ◽  
Pengfei Wang
Keyword(s):  
Spatial Distribution ◽  
Ion Channel ◽  
Optical Model ◽  
Local Potential ◽  
Sodium Ion ◽  
Spatiotemporal Processes

scholarly journals Sequential empirical Bayes method for filtering dynamic spatiotemporal processes

2017 ◽  
Vol 21 ◽  
pp. 114-129 ◽  
Author(s):  
Evangelos Evangelou ◽  
Vasileios Maroulas
Keyword(s):  
Empirical Bayes ◽  
Bayes Method ◽  
Empirical Bayes Method ◽  
Spatiotemporal Processes

scholarly journals Lived Difference: A Narrative Account of Spatiotemporal Processes of Social Differentiation

2012 ◽  
Vol 44 (9) ◽  
pp. 2049-2063 ◽  
Author(s):  
Gill Valentine ◽  
Joanna Sadgrove
Keyword(s):  
Social Differentiation ◽  
Spatiotemporal Processes ◽  
Narrative Account

Dynamic Sampling Design for Characterizing Spatiotemporal Processes in Manufacturing

2017 ◽  
Author(s):  
Chenhui Shao ◽  
Jionghua (Judy) Jin ◽  
S. Jack Hu
Keyword(s):  
High Performance ◽  
Sampling Design ◽  
Spatiotemporal Data ◽  
Progression Rate ◽  
Integer Programming Problem ◽  
Test Statistic ◽  
Design Algorithm ◽  
Dynamic Sampling ◽  
Spatiotemporal Processes ◽  
3D Measurement System

Fine-scale characterization and monitoring of spatiotemporal processes are crucial for high-performance quality control of manufacturing processes, such as ultrasonic metal welding and high-precision machining. However, it is generally expensive to acquire high-resolution spatiotemporal data in manufacturing due to the high cost of the 3D measurement system or the time-consuming measurement process. In this paper, we develop a novel dynamic sampling design algorithm to cost-effectively characterize spatiotemporal processes in manufacturing. A spatiotemporal state-space model and Kalman filter are used to predictively determine the measurement locations using a criterion considering both the prediction performance and the measurement cost. The determination of measurement locations is formulated as a binary integer programming problem, and genetic algorithm is applied for searching the optimal design. In addition, a new test statistic is proposed to monitor and update the surface progression rate. Both simulated and real-world spatiotemporal data are used to demonstrate the effectiveness of the proposed method.

scholarly journals Functional stochastic modeling and prediction of spatiotemporal processes

2003 ◽  
Vol 108 (D24) ◽  
pp. n/a-n/a ◽  
Author(s):  
M. D. Ruiz-Medina ◽  
F. J. Alonso ◽  
J. M. Angulo ◽  
M. C. Bueso
Keyword(s):  
Stochastic Modeling ◽  
Modeling And Prediction ◽  
Spatiotemporal Processes

Four-dimensional imaging and quantitative reconstruction to analyse complex spatiotemporal processes in live cells

2001 ◽  
Vol 3 (9) ◽  
pp. 852-855 ◽  
Author(s):  
Daniel Gerlich ◽  
Joël Beaudouin ◽  
Matthias Gebhard ◽  
Jan Ellenberg ◽  
Roland Eils
Keyword(s):  
Live Cells ◽  
Quantitative Reconstruction ◽  
Spatiotemporal Processes

scholarly journals Spatiotemporal processes and their implementation in Spatial System Dynamics models

2015 ◽  
Vol 60 (2) ◽  
pp. 277-288 ◽  
Author(s):  
Christian Neuwirth ◽  
Barbara Hofer ◽  
Angela Peck
Keyword(s):  
System Dynamics ◽  
Spatial System ◽  
Dynamics Models ◽  
Spatiotemporal Processes

scholarly journals Spatiotemporal processes that contribute to hydrologic exchange between hillslopes, valley bottoms, and streams

2016 ◽  
Vol 52 (6) ◽  
pp. 4628-4645 ◽  
Author(s):  
Anna Bergstrom ◽  
Kelsey Jencso ◽  
Brian McGlynn
Keyword(s):  
Hydrologic Exchange ◽  
Spatiotemporal Processes
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