The Experts below are selected from a list of 4054974 Experts worldwide ranked by ideXlab platform
Peter Jan Van Leeuwen - One of the best experts on this subject based on the ideXlab platform.
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particle filters for high dimensional Geoscience applications a review
Quarterly Journal of the Royal Meteorological Society, 2019Co-Authors: Peter Jan Van Leeuwen, Hans R Kunsch, Lars Nerger, Roland Potthast, Sebastian ReichAbstract:Particle filters contain the promise of fully nonlinear data assimilation. They have been applied in numerous science areas, including the Geosciences, but their application to high-dimensional Geoscience systems has been limited due to their inefficiency in high-dimensional systems in standard settings. However, huge progress has been made, and this limitation is disappearing fast due to recent developments in proposal densities, the use of ideas from (optimal) transportation, the use of localization and intelligent adaptive resampling strategies. Furthermore, powerful hybrids between particle filters and ensemble Kalman filters and variational methods have been developed. We present a state-of-the-art discussion of present efforts of developing particle filters for high-dimensional nonlinear Geoscience state-estimation problems, with an emphasis on atmospheric and oceanic applications, including many new ideas, derivations and unifications, highlighting hidden connections, including pseudo-code, and generating a valuable tool and guide for the community. Initial experiments show that particle filters can be competitive with present-day methods for numerical weather prediction, suggesting that they will become mainstream soon.
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particle filters for high dimensional Geoscience applications a review
arXiv: Applications, 2018Co-Authors: Peter Jan Van Leeuwen, Hans R Kunsch, Lars Nerger, Roland Potthast, Sebastian ReichAbstract:Particle filters contain the promise of fully nonlinear data assimilation. They have been applied in numerous science areas, but their application to the Geosciences has been limited due to their inefficiency in high-dimensional systems in standard settings. However, huge progress has been made, and this limitation is disappearing fast due to recent developments in proposal densities, the use of ideas from (optimal) transportation, the use of localisation and intelligent adaptive resampling strategies. Furthermore, powerful hybrids between particle filters and ensemble Kalman filters and variational methods have been developed. We present a state of the art discussion of present efforts of developing particle filters for highly nonlinear Geoscience state-estimation problems with an emphasis on atmospheric and oceanic applications, including many new ideas, derivations, and unifications, highlighting hidden connections, and generating a valuable tool and guide for the community. Initial experiments show that particle filters can be competitive with present-day methods for numerical weather prediction suggesting that they will become mainstream soon.
Sebastian Reich - One of the best experts on this subject based on the ideXlab platform.
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particle filters for high dimensional Geoscience applications a review
Quarterly Journal of the Royal Meteorological Society, 2019Co-Authors: Peter Jan Van Leeuwen, Hans R Kunsch, Lars Nerger, Roland Potthast, Sebastian ReichAbstract:Particle filters contain the promise of fully nonlinear data assimilation. They have been applied in numerous science areas, including the Geosciences, but their application to high-dimensional Geoscience systems has been limited due to their inefficiency in high-dimensional systems in standard settings. However, huge progress has been made, and this limitation is disappearing fast due to recent developments in proposal densities, the use of ideas from (optimal) transportation, the use of localization and intelligent adaptive resampling strategies. Furthermore, powerful hybrids between particle filters and ensemble Kalman filters and variational methods have been developed. We present a state-of-the-art discussion of present efforts of developing particle filters for high-dimensional nonlinear Geoscience state-estimation problems, with an emphasis on atmospheric and oceanic applications, including many new ideas, derivations and unifications, highlighting hidden connections, including pseudo-code, and generating a valuable tool and guide for the community. Initial experiments show that particle filters can be competitive with present-day methods for numerical weather prediction, suggesting that they will become mainstream soon.
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particle filters for high dimensional Geoscience applications a review
arXiv: Applications, 2018Co-Authors: Peter Jan Van Leeuwen, Hans R Kunsch, Lars Nerger, Roland Potthast, Sebastian ReichAbstract:Particle filters contain the promise of fully nonlinear data assimilation. They have been applied in numerous science areas, but their application to the Geosciences has been limited due to their inefficiency in high-dimensional systems in standard settings. However, huge progress has been made, and this limitation is disappearing fast due to recent developments in proposal densities, the use of ideas from (optimal) transportation, the use of localisation and intelligent adaptive resampling strategies. Furthermore, powerful hybrids between particle filters and ensemble Kalman filters and variational methods have been developed. We present a state of the art discussion of present efforts of developing particle filters for highly nonlinear Geoscience state-estimation problems with an emphasis on atmospheric and oceanic applications, including many new ideas, derivations, and unifications, highlighting hidden connections, and generating a valuable tool and guide for the community. Initial experiments show that particle filters can be competitive with present-day methods for numerical weather prediction suggesting that they will become mainstream soon.
Vipin Kumar - One of the best experts on this subject based on the ideXlab platform.
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Machine Learning for the Geosciences: Challenges and Opportunities
IEEE Transactions on Knowledge and Data Engineering, 2019Co-Authors: Anuj Karpatne, Imme Ebert-uphoff, Sai Ravela, Hassan A. Babaie, Vipin KumarAbstract:Geosciences is a field of great societal relevance that requires solutions to several urgent problems facing our humanity and the planet. As Geosciences enters the era of big data, machine learning (ML)—that has been widely successful in commercial domains—offers immense potential to contribute to problems in Geosciences. However, Geoscience applications introduce novel challenges for ML due to combinations of Geoscience properties encountered in every problem, requiring novel research in machine learning. This article introduces researchers in the machine learning (ML) community to these challenges offered by Geoscience problems and the opportunities that exist for advancing both machine learning and Geosciences. We first highlight typical sources of Geoscience data and describe their common properties. We then describe some of the common categories of Geoscience problems where machine learning can play a role, discussing the challenges faced by existing ML methods and opportunities for novel ML research. We conclude by discussing some of the cross-cutting research themes in machine learning that are applicable across several Geoscience problems, and the importance of a deep collaboration between machine learning and Geosciences for synergistic advancements in both disciplines.
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Machine Learning for the Geosciences: Challenges and Opportunities
arXiv: Learning, 2017Co-Authors: Anuj Karpatne, Imme Ebert-uphoff, Sai Ravela, Hassan A. Babaie, Vipin KumarAbstract:Geosciences is a field of great societal relevance that requires solutions to several urgent problems facing our humanity and the planet. As Geosciences enters the era of big data, machine learning (ML) -- that has been widely successful in commercial domains -- offers immense potential to contribute to problems in Geosciences. However, problems in Geosciences have several unique challenges that are seldom found in traditional applications, requiring novel problem formulations and methodologies in machine learning. This article introduces researchers in the machine learning (ML) community to these challenges offered by Geoscience problems and the opportunities that exist for advancing both machine learning and Geosciences. We first highlight typical sources of Geoscience data and describe their properties that make it challenging to use traditional machine learning techniques. We then describe some of the common categories of Geoscience problems where machine learning can play a role, and discuss some of the existing efforts and promising directions for methodological development in machine learning. We conclude by discussing some of the emerging research themes in machine learning that are applicable across all problems in the Geosciences, and the importance of a deep collaboration between machine learning and Geosciences for synergistic advancements in both disciplines.
Anuj Karpatne - One of the best experts on this subject based on the ideXlab platform.
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Machine Learning for the Geosciences: Challenges and Opportunities
IEEE Transactions on Knowledge and Data Engineering, 2019Co-Authors: Anuj Karpatne, Imme Ebert-uphoff, Sai Ravela, Hassan A. Babaie, Vipin KumarAbstract:Geosciences is a field of great societal relevance that requires solutions to several urgent problems facing our humanity and the planet. As Geosciences enters the era of big data, machine learning (ML)—that has been widely successful in commercial domains—offers immense potential to contribute to problems in Geosciences. However, Geoscience applications introduce novel challenges for ML due to combinations of Geoscience properties encountered in every problem, requiring novel research in machine learning. This article introduces researchers in the machine learning (ML) community to these challenges offered by Geoscience problems and the opportunities that exist for advancing both machine learning and Geosciences. We first highlight typical sources of Geoscience data and describe their common properties. We then describe some of the common categories of Geoscience problems where machine learning can play a role, discussing the challenges faced by existing ML methods and opportunities for novel ML research. We conclude by discussing some of the cross-cutting research themes in machine learning that are applicable across several Geoscience problems, and the importance of a deep collaboration between machine learning and Geosciences for synergistic advancements in both disciplines.
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Machine Learning for the Geosciences: Challenges and Opportunities
arXiv: Learning, 2017Co-Authors: Anuj Karpatne, Imme Ebert-uphoff, Sai Ravela, Hassan A. Babaie, Vipin KumarAbstract:Geosciences is a field of great societal relevance that requires solutions to several urgent problems facing our humanity and the planet. As Geosciences enters the era of big data, machine learning (ML) -- that has been widely successful in commercial domains -- offers immense potential to contribute to problems in Geosciences. However, problems in Geosciences have several unique challenges that are seldom found in traditional applications, requiring novel problem formulations and methodologies in machine learning. This article introduces researchers in the machine learning (ML) community to these challenges offered by Geoscience problems and the opportunities that exist for advancing both machine learning and Geosciences. We first highlight typical sources of Geoscience data and describe their properties that make it challenging to use traditional machine learning techniques. We then describe some of the common categories of Geoscience problems where machine learning can play a role, and discuss some of the existing efforts and promising directions for methodological development in machine learning. We conclude by discussing some of the emerging research themes in machine learning that are applicable across all problems in the Geosciences, and the importance of a deep collaboration between machine learning and Geosciences for synergistic advancements in both disciplines.
Roland Potthast - One of the best experts on this subject based on the ideXlab platform.
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particle filters for high dimensional Geoscience applications a review
Quarterly Journal of the Royal Meteorological Society, 2019Co-Authors: Peter Jan Van Leeuwen, Hans R Kunsch, Lars Nerger, Roland Potthast, Sebastian ReichAbstract:Particle filters contain the promise of fully nonlinear data assimilation. They have been applied in numerous science areas, including the Geosciences, but their application to high-dimensional Geoscience systems has been limited due to their inefficiency in high-dimensional systems in standard settings. However, huge progress has been made, and this limitation is disappearing fast due to recent developments in proposal densities, the use of ideas from (optimal) transportation, the use of localization and intelligent adaptive resampling strategies. Furthermore, powerful hybrids between particle filters and ensemble Kalman filters and variational methods have been developed. We present a state-of-the-art discussion of present efforts of developing particle filters for high-dimensional nonlinear Geoscience state-estimation problems, with an emphasis on atmospheric and oceanic applications, including many new ideas, derivations and unifications, highlighting hidden connections, including pseudo-code, and generating a valuable tool and guide for the community. Initial experiments show that particle filters can be competitive with present-day methods for numerical weather prediction, suggesting that they will become mainstream soon.
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particle filters for high dimensional Geoscience applications a review
arXiv: Applications, 2018Co-Authors: Peter Jan Van Leeuwen, Hans R Kunsch, Lars Nerger, Roland Potthast, Sebastian ReichAbstract:Particle filters contain the promise of fully nonlinear data assimilation. They have been applied in numerous science areas, but their application to the Geosciences has been limited due to their inefficiency in high-dimensional systems in standard settings. However, huge progress has been made, and this limitation is disappearing fast due to recent developments in proposal densities, the use of ideas from (optimal) transportation, the use of localisation and intelligent adaptive resampling strategies. Furthermore, powerful hybrids between particle filters and ensemble Kalman filters and variational methods have been developed. We present a state of the art discussion of present efforts of developing particle filters for highly nonlinear Geoscience state-estimation problems with an emphasis on atmospheric and oceanic applications, including many new ideas, derivations, and unifications, highlighting hidden connections, and generating a valuable tool and guide for the community. Initial experiments show that particle filters can be competitive with present-day methods for numerical weather prediction suggesting that they will become mainstream soon.
