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Machine Learning for Predicting Chaotic Systems
v1v2v3 (latest)

Machine Learning for Predicting Chaotic Systems

29 July 2024
Christof Schötz
Alistair J R White
Maximilian Gelbrecht
Niklas Boers
    AI4CE
ArXiv (abs)PDFHTML

Papers citing "Machine Learning for Predicting Chaotic Systems"

12 / 12 papers shown
Title
Projected Neural Differential Equations for Learning Constrained
  Dynamics
Projected Neural Differential Equations for Learning Constrained Dynamics
Alistair J R White
Anna Buttner
Maximilian Gelbrecht
Valentin Duruisseaux
Niki Kilbertus
Frank Hellmann
Niklas Boers
296
2
0
31 Oct 2024
Zero-shot forecasting of chaotic systems
Zero-shot forecasting of chaotic systemsInternational Conference on Learning Representations (ICLR), 2024
Yuanzhao Zhang
William Gilpin
AI4TS
611
16
0
24 Sep 2024
Model scale versus domain knowledge in statistical forecasting of
  chaotic systems
Model scale versus domain knowledge in statistical forecasting of chaotic systemsPhysical Review Research (Phys. Rev. Res.), 2023
W. Gilpin
AI4TS
315
33
0
13 Mar 2023
Are Transformers Effective for Time Series Forecasting?
Are Transformers Effective for Time Series Forecasting?AAAI Conference on Artificial Intelligence (AAAI), 2022
Ailing Zeng
Mu-Hwa Chen
L. Zhang
Qiang Xu
AI4TS
418
2,802
0
26 May 2022
Chaos as an interpretable benchmark for forecasting and data-driven
  modelling
Chaos as an interpretable benchmark for forecasting and data-driven modelling
W. Gilpin
AI4TS
276
105
0
11 Oct 2021
Next Generation Reservoir Computing
Next Generation Reservoir ComputingNature Communications (Nat Commun), 2021
D. Gauthier
Erik Bollt
Aaron Griffith
W. A. S. Barbosa
272
513
0
14 Jun 2021
Backpropagation Algorithms and Reservoir Computing in Recurrent Neural
  Networks for the Forecasting of Complex Spatiotemporal Dynamics
Backpropagation Algorithms and Reservoir Computing in Recurrent Neural Networks for the Forecasting of Complex Spatiotemporal DynamicsNeural Networks (NN), 2019
Pantelis R. Vlachas
Jaideep Pathak
Brian R. Hunt
T. Sapsis
M. Girvan
Edward Ott
Petros Koumoutsakos
AI4TS
269
442
0
09 Oct 2019
N-BEATS: Neural basis expansion analysis for interpretable time series
  forecasting
N-BEATS: Neural basis expansion analysis for interpretable time series forecastingInternational Conference on Learning Representations (ICLR), 2019
Boris N. Oreshkin
Dmitri Carpov
Nicolas Chapados
Yoshua Bengio
AI4TS
625
1,374
0
24 May 2019
Neural Ordinary Differential Equations
Neural Ordinary Differential Equations
T. Chen
Yulia Rubanova
J. Bettencourt
David Duvenaud
AI4CE
991
6,118
0
19 Jun 2018
Learning unknown ODE models with Gaussian processes
Learning unknown ODE models with Gaussian processesInternational Conference on Machine Learning (ICML), 2018
Markus Heinonen
Çağatay Yıldız
Henrik Mannerstrom
Jukka Intosalmi
Harri Lähdesmäki
189
108
0
12 Mar 2018
Hybrid Forecasting of Chaotic Processes: Using Machine Learning in
  Conjunction with a Knowledge-Based Model
Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model
Jaideep Pathak
Alexander Wikner
Rebeckah K. Fussell
Sarthak Chandra
Brian Hunt
M. Girvan
Edward Ott
157
329
0
09 Mar 2018
Attention Is All You Need
Attention Is All You NeedNeural Information Processing Systems (NeurIPS), 2017
Ashish Vaswani
Noam M. Shazeer
Niki Parmar
Jakob Uszkoreit
Llion Jones
Aidan Gomez
Lukasz Kaiser
Illia Polosukhin
3DV
2.6K
158,962
0
12 Jun 2017
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