Functional Data Analysis - Revision history

Wiki: /* State Space Reconstruction */

2026-02-10T12:13:34Z

State Space Reconstruction

2026-02-08T12:03:37Z

Practical spatial-time series

2026-02-08T12:01:43Z

References

2026-02-08T12:01:19Z

Data collections

2026-02-08T11:59:53Z

2025-11-24T22:08:29Z

State Space Reconstruction

2025-11-24T22:06:07Z

State Space Reconstruction

2025-11-24T22:02:03Z

State Space Reconstruction

2025-11-23T23:02:27Z

PINN Libraries

2025-11-23T23:02:01Z

Physics-Informed Neural Networks

← Older revision		Revision as of 12:13, 10 February 2026
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	# State-space representation [https://en.wikipedia.org/wiki/State-space_representation wiki]		# State-space representation [https://en.wikipedia.org/wiki/State-space_representation wiki]
	# Phase space [https://en.wikipedia.org/wiki/Phase_space wiki]		# Phase space [https://en.wikipedia.org/wiki/Phase_space wiki]
		+
		+	=== Collection===
		+	# Time-SSM: Simplifying and Unifying State Space Models for Time Series Forecasting [https://arxiv.org/pdf/2405.16312 arxiv]
		+	# Time-series forecasting using manifold learning, radial basis function interpolation, and geometric harmonics [arxiv]
		+	# srush/annotated-s4
		+	# Modeling Nonlinear Dynamics from Equations and Data by George Haller [https://epubs.siam.org/doi/book/10.1137/1.9781611978353 book] [https://www.youtube.com/watch?v=mhcZaBMeA-U youtube]

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 ==Practical spatial-time series==
 # A guide to state–space modeling of ecological time series, 2021 [https://doi.org/10.1002/ecm.1470 PDF], (Bayesian Kalman)
 # Kalman Filtering and Smoothing, 2025 [https://arxiv.org/pdf/2405.08971 ArXiv]  (Riemannian Kalman)
 ===General===

@@ Line 219: / Line 219: @@
 # ClimateSet, 2023 [https://arxiv.org/pdf/2311.03721 ArXiv]
 ===General===
 # Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems [https://arxiv.org/abs/2307.08423 arxiv 2023]

← Older revision		Revision as of 12:01, 8 February 2026
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	# Kalman Filtering and Smoothing, 2025 [https://arxiv.org/pdf/2405.08971 ArXiv] (Riemannian Kalman)		# Kalman Filtering and Smoothing, 2025 [https://arxiv.org/pdf/2405.08971 ArXiv] (Riemannian Kalman)

−	~~==Data collections==~~
	# ClimateSet, 2023 [https://arxiv.org/pdf/2311.03721 ArXiv]		# ClimateSet, 2023 [https://arxiv.org/pdf/2311.03721 ArXiv]

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 # The false nearest neighbors algorithm by Carl Rhodes [https://doi.org/10.1016/S0098-1354(97)87657-0 doi 1997], [https://journals.aps.org/pre/abstract/10.1103/PhysRevE.55.6162 Carl's another paper]
 # Use of False Nearest Neighbours for Selecting Variables andEmbedding Parameters for State Space Reconstruction by Anna Krakovská et al. [https://onlinelibrary.wiley.com/doi/epdf/10.1155/2015/932750 doi 2014]
 # ODE. Differential manifolds by Vladimir Arnold (last chapter of the textbook)
 # ODE by Ilya Shchurov [https://ode.mathbook.info/chapter/label/chap:10prim:linearization/ www]
 # State-space representation [https://en.wikipedia.org/wiki/State-space_representation wiki]
 # Phase space [https://en.wikipedia.org/wiki/Phase_space wiki]

@@ Line 393: / Line 393: @@
 ====PINN Libraries ====
-# PINA Gianluigi Rozza at SISSA MathLab [https://mathlab.github.io/PINA/_rst/_code.html www]
+# PINA Gianluigi Rozza at SISSA MathLab [https://mathlab.github.io/PINA/_rst/_code.html www] see tutorials and solvers
 === Riemmanian models===

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 # Physics-based Deep Learning [https://www.physicsbaseddeeplearning.org/intro.html github]
 # Integral Transforms in a Physics-Informed (Quantum) Neural Network setting [https://arxiv.org/pdf/2206.14184 arxiv 2022]
-# Lectures ny Stephen Brunton [https://www.youtube.com/watch?v=fiX8c-4K0-Q&list=PLMrJAkhIeNNQ0BaKuBKY43k4xMo6NSbBa&index=5 AI/ML+Physics], [https://www.youtube.com/watch?v=3SNkQ8jhKXc&list=PLMrJAkhIeNNQ0BaKuBKY43k4xMo6NSbBa&index=7 Part 4], [https://www.youtube.com/watch?v=nmvs0vrBT18 Basic PDEs], [https://www.youtube.com/watch?v=pvrIagjEk4c PDE Overview],
+# Lectures ny Stephen Brunton [https://www.youtube.com/watch?v=fiX8c-4K0-Q&list=PLMrJAkhIeNNQ0BaKuBKY43k4xMo6NSbBa&index=5 AI/ML+Physics], [https://www.youtube.com/watch?v=3SNkQ8jhKXc&list=PLMrJAkhIeNNQ0BaKuBKY43k4xMo6NSbBa&index=7 Part 4], [https://www.youtube.com/watch?v=nmvs0vrBT18 Basic PDEs], [https://www.youtube.com/watch?v=pvrIagjEk4c PDE Overview]
 === Riemmanian models===