MLDADS
Machine Learning and Data Assimilation for Dynamical Systems is our flagship data learning workshop.
Here you can access the content of the most recent edition, MLDADS 2021.
Why the MLDADS workshop
The theory of dynamical systems (DS) addresses the qualitative behaviour of dynamical systems as understood from models. Models are imperfect but can be improved using data and tools from the field of data assimilation (DA).
Machine learning (ML) develops algorithms to accomplish certain tasks, and improve their performance with the input of more data.
The intersection of the fields of dynamical systems, data assimilation and machine learning is still largely unexplored. The goal of this workshop is to bring together researchers from these fields, share knowledge and expertise, and fill the gap between these theories.
In this short video Dr Rossella Arcucci presents the vision behind this workshop.
MLDADS 2021 Welcome from Dr Arcucci
Talks
Here below you can find the 18 shortlisted talks for the 2021 edition. You can access both the abstract and the video recording, as delivered by our speakers.
You can also watch the videos directly on the Data Learning You Tube channel.
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Deep learning for solar irradiance nowcasting: a comparison of a recurrent neural network and two traditional methods (abstract, talk) by Dennis Knol, Fons de Leeuw, Jan Fokke Meirink and Valeria Krzhizhanovskaya
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Using machine learning to correct model error in data assimilation and forecast applications (abstract, talk) by Alban Farchi, Patrick Laloyaux, Massimo Bonavita and Marc Bocquet
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Real-time probabilistic inversion of DNN-based DeepEM model while accounting for model error (abstract, talk) by Muzammil Hussain Rammay, Sergey Alyaev, Ahmed H. Elsheikh and Reidar Brumer Bratvold
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Latent GAN: using a latent space-based GAN for rapid forecasting of CFD models (abstract, talk) by Jamal Afzali, Cesar Quilodran Casas and Rossella Arcucci
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Automatic-differentiated Physics-Informed Echo State Network (API-ESN) (abstract, talk) by Alberto Racca and Luca Magri
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Intelligent camera cloud operators for convective scale numerical weather prediction (abstract, talk) by Maria Reinhardt, Sybille Schoger, Frederik Kurzrock, Roland Potthast and Louis-Etienne Boudreault
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A machine learning method for parameter estimation and sensitivity analysis (abstract, talk) by Marcella Torres
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From macro to micro and back: Microstates initialization from chaotic aggregate time series (abstract, talk) by Blas Kolic, Juan Sabuco and J. Doyne Farmer
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Low-dimensional decompositions for nonlinear finite impulse response modeling (abstract, talk) by Maciej Filiński, Paweł Wachel and Koen Tiels
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Auto-encoded reservoir computing for turbulence learning (abstract, talk) by Nguyen Anh Khoa Doan, Wolfgang Polifke and Luca Magri
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Data assimilation in the latent space of a convolutional autoencoder (abstract, talk) by Maddalena Amendola, Rossella Arcucci, Laetitia Mottet, Cesar Quilodran Casas, Shiwei Fan, Christopher Pain, Paul Linden and Yi-Ke Guo
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Higher-order hierarchical spectral clustering for multidimensional data (abstract, talk) by Giuseppe Brandi and Tiziana Di Matteo
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Neural networks for conditioning surface-based geological models with uncertainty analysis (abstract, talk) by Zainab Titus, Claire Heaney, Carl Jacquemyn, Pablo Salinas, Matthew Jackson and Christopher Pain
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Towards data-driven simulation models for building energy management (abstract, talk) by Juan Gomez Romero and Miguel Molina-Solana
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Data assimilation using heteroscedastic bayesian neural network ensembles for reduced-order flame models (abstract, talk) by Maximilian Croci, Ushnish Sengupta and Matthew Juniper
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A GPU algorithm for outliers detection in TESS light curves (abstract, talk) by Stefano Fiscale, Pasquale De Luca, Laura Inno, Livia Marcellino, Ardelio Galletti, Alessandra Rotundi, Angelo Ciaramella, Giovanni Covone and Elisa Quintana
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Data-driven deep learning emulators for geophysical forecasting (abstract, talk) by Varuni Katti Sastry, Romit Maulik, Vishwas Hebbur Venkata Subba Rao, Sudarshan Ashwin Renganathan and Rao Kotamarthi
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NVIDIA SimNet™: An AI-Accelerated Multi-Physics Simulation Framework (abstract, talk) by Oliver Hennigh, Susheela Narasimhan, Mohammad Amin Nabian, Akshay Subramaniam, Kaustubh Tangsali, Max Rietmann, Jose del Aguila Ferrandis, Wonmin Byeon, Zhiwei Fang and Sanjay Choudhry