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- id: GuthEvaluation2023
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- id: HanSolving2018
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- id: LaloyEmulation2019
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- id: LuDeepXDE2021
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- id: MoDeep2019
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    - family: Wu
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    Deep Autoregressive Neural Networks for High-Dimensional Inverse Problems in
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- id: MullerDeep2020
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      day: 19
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  publisher: arXiv
  title: Deep Ritz revisited
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- id: NabianDeep2019
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      month: 9
      day: 16
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- id: ParkMachine2019
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  event-title: NeurIPS
  issued:
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  language: en
  page: '5'
  title: Machine learning climate variability
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- id: PirinenFully2023
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  publisher: arXiv
  title: >-
    Fully Convolutional Networks for Dense Water Flow Intensity Prediction in
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- id: RackauckasGeneralized2020
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  page: '6'
  title: >-
    Generalized Physics-Informed Learning Through Language-Wide Differentiable
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- id: RaissiHidden2020
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    Hidden fluid mechanics: Learning velocity and pressure fields from flow
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- id: RaissiPhysicsinformed2019
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  citation-key: RaissiPhysicsinformed2019
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  title: >-
    Physics-informed neural networks: A deep learning framework for solving
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- id: RazaviDeep2021
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    - family: Hornegger
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    - family: Wells
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  language: en
  page: 234-241
  publisher: Springer International Publishing
  publisher-place: Cham
  title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
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- id: RuthottoDeep2020
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    - family: Main-Knorn
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    Ten deep learning techniques to address small data problems with remote
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  container-title: Artificial Intelligence and Statistics
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    Infinite-Dimensional Kalman Filtering Approach to Spatio-Temporal Gaussian
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  citation-key: SarkkaLinear2011
  collection-title: Lecture Notes in Computer Science
  container-title: Artificial Neural Networks and Machine Learning – ICANN 2011
  DOI: 10.1007/978-3-642-21738-8_20
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    - family: Duch
      given: Włodzisław
    - family: Girolami
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  event-title: International Conference on Artificial Neural Networks
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  language: en
  page: 151-158
  publisher: Springer
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- id: SarkkaSpatiotemporal2013
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    Spatiotemporal Learning via Infinite-Dimensional Bayesian Filtering and
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- id: ShankarLearning2020
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  container-title: Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020)
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  title: Learning non-linear spatio-temporal dynamics with convolutional Neural ODEs
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- id: ShelhamerFully2017
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  page: 640-651
  title: Fully Convolutional Networks for Semantic Segmentation
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- id: SolinInfinitedimensional2013
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- id: TaitVariational2020
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  container-title: arXiv:2006.15641 [cs, stat]
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- id: TartakovskyLearning2018
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  publisher: Society for Industrial and Applied Mathematics
  title: >-
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  container-title: Spatial Statistics
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- id: ZammitMangionDeep2021
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- id: ZangWeak2020
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      day: 16
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    - family: Zhou
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  container-title: Journal of Computational Physics
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- id: ZhangLearning2020
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      day: 20
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  container-title: SIAM Journal on Scientific Computing
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- id: ZhangQuantifying2019
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...
