The theory of data assimilation and machine learning is introduced in an accessible and pedagogical manner, with a focus on the underlying statistical physics. This modern and cross-disciplinary book is suitable for undergraduate and graduate students from science and engineering without specialized experience of statistical physics.
The theory of data assimilation and machine learning is introduced in an accessible and pedagogical manner, with a focus on the underlying statistical physics. This modern and cross-disciplinary book is suitable for undergraduate and graduate students from science and engineering without specialized experience of statistical physics.
Henry D. I. Abarbanel has worked in several fields of physics including high energy physics, nonlinear dynamics, and data assimilation in neurobiology. He is the author of two previous books: Analysis of Observed Chaotic Data (1996) and Predicting the Future: Completing Models of Observed Complex Systems (2013). He is a Distinguished Professor of Physics at University of California, San Diego (UCSD) and a Distinguished Research Physicist at UCSD's Scripps Institution of Oceanography.
Inhaltsangabe
1. Prologue: linking 'The Future' with the present 2. A data assimilation reminder 3. Remembrance of things path 4. SDA variational principles Euler-Lagrange equations and Hamiltonian formulation 5. Using waveform information 6. Annealing in the model precision Rf 7. Discrete time integration in data assimilation variational principles Lagrangian and Hamiltonian formulations 8. Monte Carlo methods 9. Machine learning and its equivalence to statistical data assimilation 10. Two examples of the practical use of data assimilation 11. Unfinished business Bibliography Index.
1. Prologue: linking 'The Future' with the present 2. A data assimilation reminder 3. Remembrance of things path 4. SDA variational principles Euler-Lagrange equations and Hamiltonian formulation 5. Using waveform information 6. Annealing in the model precision Rf 7. Discrete time integration in data assimilation variational principles Lagrangian and Hamiltonian formulations 8. Monte Carlo methods 9. Machine learning and its equivalence to statistical data assimilation 10. Two examples of the practical use of data assimilation 11. Unfinished business Bibliography Index.
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