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A Taylor Based Sampling Scheme for Machine Learning in Computational
  Physics
v1v2 (latest)

A Taylor Based Sampling Scheme for Machine Learning in Computational Physics

20 January 2021
Paul Novello
Gaël Poëtte
D. Lugato
P. Congedo
    PINNAI4CE
ArXiv (abs)PDFHTML

Papers citing "A Taylor Based Sampling Scheme for Machine Learning in Computational Physics"

4 / 4 papers shown
Title
Safe Active Learning for Time-Series Modeling with Gaussian Processes
Safe Active Learning for Time-Series Modeling with Gaussian Processes
Christoph Zimmer
Mona Meister
D. Nguyen-Tuong
AI4TS
65
52
0
09 Feb 2024
DeepXDE: A deep learning library for solving differential equations
DeepXDE: A deep learning library for solving differential equations
Lu Lu
Xuhui Meng
Zhiping Mao
George Karniadakis
PINNAI4CE
97
1,533
0
10 Jul 2019
Deep Bayesian Active Learning with Image Data
Deep Bayesian Active Learning with Image Data
Y. Gal
Riashat Islam
Zoubin Ghahramani
BDLUQCV
70
1,735
0
08 Mar 2017
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed
  Systems
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Martín Abadi
Ashish Agarwal
P. Barham
E. Brevdo
Zhiwen Chen
...
Pete Warden
Martin Wattenberg
Martin Wicke
Yuan Yu
Xiaoqiang Zheng
276
11,151
0
14 Mar 2016
1