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Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning
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Yufei Wang Ziju Shen Zichao Long & Bin Dong 《Communications In Computational Physics》2020,28(5):2158-2179
Conservation laws are considered to be fundamental laws of nature. It has
broad applications in many fields, including physics, chemistry, biology, geology, and
engineering. Solving the differential equations associated with conservation laws is a
major branch in computational mathematics. The recent success of machine learning,
especially deep learning in areas such as computer vision and natural language processing, has attracted a lot of attention from the community of computational mathematics and inspired many intriguing works in combining machine learning with traditional methods. In this paper, we are the first to view numerical PDE solvers as an
MDP and to use (deep) RL to learn new solvers. As proof of concept, we focus on
1-dimensional scalar conservation laws. We deploy the machinery of deep reinforcement learning to train a policy network that can decide on how the numerical solutions should be approximated in a sequential and spatial-temporal adaptive manner.
We will show that the problem of solving conservation laws can be naturally viewed
as a sequential decision-making process, and the numerical schemes learned in such a
way can easily enforce long-term accuracy. Furthermore, the learned policy network
is carefully designed to determine a good local discrete approximation based on the
current state of the solution, which essentially makes the proposed method a meta-learning approach. In other words, the proposed method is capable of learning how to
discretize for a given situation mimicking human experts. Finally, we will provide details on how the policy network is trained, how well it performs compared with some
state-of-the-art numerical solvers such as WENO schemes, and supervised learning
based approach L3D and PINN, and how well it generalizes. 相似文献
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Amy Y. Zhang Christopher Burant Alex Z. Fu Gerald Strauss Donald R. Bodner Lee Ponsky 《Journal of psychosocial oncology》2020,38(2):210-227
AbstractPurpose: We examined underlying psychosocial processes of a behavioral treatment for urinary incontinence (UI) of prostate cancer survivors.Design: Secondary analysis of data collected from a clinical trial.Sample: Two hundred forty-four prostate cancer survivors who participated in a clinical trial of behavioral intervention to UI as intervention or control subjects.Methods: The participants had a 3-month behavioral intervention or usual care and were followed up for an additional 3?months. They were assessed at baseline, 3, and 6?months. Latent growth curve models were performed to examine trajectories of each study variable and relationships among the variables.Findings: Increasing self-efficacy and social support were significantly and independently associated with more reduction of urinary leakage frequency over time.Implications for psychosocial oncology: Providing problem-solving skills and social support, including peer support, are essential for empowering patients to reduce UI. 相似文献
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Prevalence of Potentially Inappropriate Prescribing Among Hong Kong Older Adults: A Comparison of the Beers 2003, Beers 2012, and Screening Tool of Older Person's Prescriptions and Screening Tool to Alert doctors to Right Treatment Criteria
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![点击此处可从《Journal of the American Geriatrics Society》网站下载免费的PDF全文](/ch/ext_images/free.gif)
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