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Data-Driven Methods for Inverse Problems : = Blending Data Assimilation and Machine Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data-Driven Methods for Inverse Problems :/
其他題名:	Blending Data Assimilation and Machine Learning.
作者:	Chen, Yuming.
面頁冊數:	1 online resource (253 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
Contained By:	Dissertations Abstracts International84-12B.
標題:	Applied mathematics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30426541click for full text (PQDT)
ISBN:	9798379705022

Data-Driven Methods for Inverse Problems : = Blending Data Assimilation and Machine Learning.
Chen, Yuming.

Data-Driven Methods for Inverse Problems :Blending Data Assimilation and Machine Learning. - 1 online resource (253 pages)

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

Thesis (Ph.D.)--The University of Chicago, 2023.

Includes bibliographical references

Inverse problems (IPs) deal with the task of reconstructing input variables from noisy observations, defined through a forward model. When the forward map is known, the reconstruction of input variables can be performed via numerous approaches, including optimization-based algorithms (e.g., maximum likelihood estimation) and sampling-based algorithms (e.g., Markov chain Monte Carlo). However, there are scenarios where: (1) we do not have perfect knowledge about the forward model; or (2) the forward model is expensive to simulate, which severely limits the implementation of optimization and sampling algorithms that may require multiple forward model simulations. Therefore, in these scenarios, it is essential to approximate the forward model by a parameterized surrogate model. We propose data-driven approaches that jointly learn the parameters of the surrogate model, and reconstruct input variables, from observation data alone.Data assimilation (DA) deals with the task of reconstructing temporally evolving hidden states from noisy time series observations, defined through a state space model (SSM). When the SSM is known, the reconstruction of states can be performed using optimization-based algorithms (e.g., 4DVAR) or sampling-based algorithms (e.g., particle filtering). However, similar to IPs, there are scenarios where: (1) we do not have perfect knowledge about the SSM; or (2) the SSM is expensive to simulate, which increases the computational cost of the reconstruction algorithms. Therefore, in these scenarios, it is essential to approximate the SSM with a parameterized surrogate model. We employ machine learning techniques and propose data-driven approaches that jointly learn the parameters of the surrogate model, and reconstruct the states, from observation data alone.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379705022Subjects--Topical Terms:

2122814
Applied mathematics.
Subjects--Index Terms:

Bayesian inferenceIndex Terms--Genre/Form:

542853
Electronic books.

Data-Driven Methods for Inverse Problems : = Blending Data Assimilation and Machine Learning.
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