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Operator inference of non-Markovian terms for learning reduced models
  from partially observed state trajectories
v1v2 (latest)

Operator inference of non-Markovian terms for learning reduced models from partially observed state trajectories

Journal of Scientific Computing (J. Sci. Comput.), 2021
1 March 2021
W. I. Uy
Benjamin Peherstorfer
    OffRL
ArXiv (abs)PDFHTML

Papers citing "Operator inference of non-Markovian terms for learning reduced models from partially observed state trajectories"

4 / 4 papers shown
Nested Operator Inference for Adaptive Data-Driven Learning of Reduced-order Models
Nested Operator Inference for Adaptive Data-Driven Learning of Reduced-order Models
Nicole Aretz
Karen Willcox
AI4CE
186
2
0
15 Aug 2025
Operator inference with roll outs for learning reduced models from
  scarce and low-quality data
Operator inference with roll outs for learning reduced models from scarce and low-quality dataComputers and Mathematics with Applications (CMA), 2022
W. I. Uy
D. Hartmann
Benjamin Peherstorfer
AI4CE
294
23
0
02 Dec 2022
Active operator inference for learning low-dimensional dynamical-system
  models from noisy data
Active operator inference for learning low-dimensional dynamical-system models from noisy dataSIAM Journal on Scientific Computing (SISC), 2021
W. I. Uy
Yuepeng Wang
Yuxiao Wen
Benjamin Peherstorfer
AI4CE
195
12
0
20 Jul 2021
Physics-informed regularization and structure preservation for learning
  stable reduced models from data with operator inference
Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inference
N. Sawant
Boris Kramer
Benjamin Peherstorfer
AI4CE
205
44
0
06 Jul 2021
1
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