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Chaos as an interpretable benchmark for forecasting and data-driven
  modelling

Chaos as an interpretable benchmark for forecasting and data-driven modelling

11 October 2021
W. Gilpin
    AI4TS
ArXivPDFHTML

Papers citing "Chaos as an interpretable benchmark for forecasting and data-driven modelling"

13 / 13 papers shown
Title
Optimizing Hard Thresholding for Sparse Model Discovery
Optimizing Hard Thresholding for Sparse Model Discovery
Derek W. Jollie
Scott G. McCalla
39
0
0
28 Apr 2025
Neural ODE Transformers: Analyzing Internal Dynamics and Adaptive Fine-tuning
Neural ODE Transformers: Analyzing Internal Dynamics and Adaptive Fine-tuning
Anh Tong
Thanh Nguyen-Tang
Dongeun Lee
Duc Nguyen
Toan M. Tran
David Hall
Cheongwoong Kang
Jaesik Choi
33
0
0
03 Mar 2025
Channel Dependence, Limited Lookback Windows, and the Simplicity of Datasets: How Biased is Time Series Forecasting?
Channel Dependence, Limited Lookback Windows, and the Simplicity of Datasets: How Biased is Time Series Forecasting?
Ibram Abdelmalak
Kiran Madhusudhanan
Jungmin Choi
Maximilian Stubbemann
Lars Schmidt-Thieme
AI4TS
65
1
0
17 Feb 2025
Zero-shot forecasting of chaotic systems
Zero-shot forecasting of chaotic systems
Yuanzhao Zhang
William Gilpin
AI4TS
37
4
0
24 Sep 2024
Machine Learning for Predicting Chaotic Systems
Machine Learning for Predicting Chaotic Systems
Christof Schötz
Alistair J R White
Maximilian Gelbrecht
Niklas Boers
AI4CE
22
4
0
29 Jul 2024
Nearest Neighbors GParareal: Improving Scalability of Gaussian Processes
  for Parallel-in-Time Solvers
Nearest Neighbors GParareal: Improving Scalability of Gaussian Processes for Parallel-in-Time Solvers
Guglielmo Gattiglio
Lyudmila Grigoryeva
M. Tamborrino
24
1
0
20 May 2024
ODEFormer: Symbolic Regression of Dynamical Systems with Transformers
ODEFormer: Symbolic Regression of Dynamical Systems with Transformers
Stéphane d’Ascoli
Soren Becker
Alexander Mathis
Philippe Schwaller
Niki Kilbertus
13
21
0
09 Oct 2023
Evaluating generation of chaotic time series by convolutional generative
  adversarial networks
Evaluating generation of chaotic time series by convolutional generative adversarial networks
Y. Tanaka
Y. Yamaguti
8
2
0
26 May 2023
AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for
  Approximating Reynolds-Averaged Navier-Stokes Solutions
AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutions
F. Bonnet
Jocelyn Ahmed Mazari
Paola Cinnella
Patrick Gallinari
AI4CE
25
54
0
15 Dec 2022
Experimental study of Neural ODE training with adaptive solver for
  dynamical systems modeling
Experimental study of Neural ODE training with adaptive solver for dynamical systems modeling
A. Allauzen
Thiago Petrilli Maffei Dardis
Hannah Plath
AI4CE
14
0
0
13 Nov 2022
Unsupervised Learned Kalman Filtering
Unsupervised Learned Kalman Filtering
Guy Revach
Nir Shlezinger
Timur Locher
Xiaoyong Ni
Ruud J. G. van Sloun
Yonina C. Eldar
SSL
18
30
0
18 Oct 2021
KalmanNet: Neural Network Aided Kalman Filtering for Partially Known
  Dynamics
KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics
Guy Revach
Nir Shlezinger
Xiaoyong Ni
Adrià López Escoriza
Ruud J. G. van Sloun
Yonina C. Eldar
20
258
0
21 Jul 2021
Informer: Beyond Efficient Transformer for Long Sequence Time-Series
  Forecasting
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Haoyi Zhou
Shanghang Zhang
J. Peng
Shuai Zhang
Jianxin Li
Hui Xiong
Wan Zhang
AI4TS
164
3,799
0
14 Dec 2020
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