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Articles by J. Nathan
Electrical Engineering and Systems Science > Systems and Control
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Post-processed Antarctic OSI-SAF v2 data for training IceDMD model
Published April 21, 2026 | Version v1 Dataset Open This dataset provides a temporally complete version of the OSI SAF Global Sea Ice Concentration (Version 2.0). The primary objective of this derivative product is to resolve the data gaps inherent in the original satellite records, facilitating continuous time-series analysis for modelling and training prediction models (including IceDMD). The original OSI SAF v2.0 product, while highly accurate, contains intermittent missing days.
Electrical Engineering and Systems Science > Systems and Control
[Submitted on 7 Apr 2026 (v1), last revised 25 Sep 2026 (this version, v2)] View PDF HTML (experimental) Comments: Subjects: Systems and Control (eess.SY); Machine Learning (cs.LG) Cite as: arXiv:2604.05904 [eess.SY] (or arXiv:2604.05904v2 [eess.SY] for this version) https://doi.org/10.48550/arXiv.2604.05904
Computer Science > Machine Learning
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Cheap2Rich: A Multi-Fidelity Framework for Data Assimilation and System Identification of Multiscale Physics -- Rotating Detonation Engines
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[2601.10208] Terrain-Adaptive Mobile 3D Printing with Hierarchical Control
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
[2512.09090] A Taxonomy of Numerical Differentiation Methods
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
[2511.04001] Accelerating scientific discovery with the common task framework
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
[2510.18037] Benchmarking Probabilistic Time Series Forecasting Models on Neural Activity
arXiv:2510.18037 (cs) View PDF HTML (experimental) Comments: Subjects: Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC); Machine Learning (stat.ML) Cite as: arXiv:2510.18037 [cs.LG] (or arXiv:2510.18037v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2510.18037 Submission history From: Ziyu Lu [ view email] [v1] Mon, 20 Oct 2025 19:19:29 UTC (2,341 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Connected Papers Toggle Connected Papers...
[2505.16058] Mesh-free sparse identification of nonlinear dynamics
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
From Models To Experiments: Shallow Recurrent Decoder Networks on the DYNASTY Experimental Facility
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
[2503.08904] Towards Efficient Parametric State Estimation in Circulating Fuel Reactors with Shallow Recurrent Decoder Networks
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[2502.09490] Inverse Design with Dynamic Mode Decomposition
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
[2410.16397] Towards a Reliable Offline Personal AI Assistant for Long Duration Spaceflight
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Mobile Sensor Path Planning for Kalman Filter Spatiotemporal Estimation
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VENI, VINDy, VICI: a generative reduced-order modeling framework with uncertainty quantification
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
[2403.01723] Statistical Mechanics of Dynamical System Identification
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
[2402.09234] Multi-Hierarchical Surrogate Learning for Structural Dynamical Crash Simulations Using Graph Convolutional Neural Networks
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Capturing excited-state structural snapshots of evolutionary green-to-red photochromic fluorescent proteins
1 Introduction Photoconvertible and photoswitchable fluorescent proteins (FPs) are genetically encodable phototransformable biomarkers that can irreversibly and reversibly alter their electronic absorption and emission spectral features, respectively, upon light exposure of specific wavelengths (Patterson and Lippincott-Schwartz, 2002; Fernández-Suárez and Ting, 2008; Chudakov et al., 2010; Jung, 2012; Zhou and Lin, 2013; Adam et al., 2014; Milo and Phillips, 2015; Acharya et al., 2017).
Submission of the Citizens and Technology (CAT) Lab to the United States Copyright Office, U.S .Library of Congress, Washington, D.C., re Notice of Inquiry Technical Measures Public Consultations [ Docket No. 2021–10 ][ Federal Reg. No: 2021-27705 ]
Repository Citation Matias, J. Nathan; Penney, Jonathon W.; and Wright, Lucas, "Submission of the Citizens and Technology (CAT) Lab to the United States Copyright Office, U.S .Library of Congress, Washington, D.C., re Notice of Inquiry Technical Measures Public Consultations [ Docket No. 2021–10 ][ Federal Reg. No: 2021-27705 ]" (2022). Commissioned Reports, Studies and Public Policy Documents. Paper 233. https://digitalcommons.osgoode.yorku.ca/reports/233
[2309.00325] Multi-fidelity reduced-order surrogate modeling
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
[2308.04405] Nonlinear parametric models of viscoelastic fluid flows
[Submitted on 8 Aug 2023 ( v1 ), last revised 1 Dec 2023 (this version, v2)] Title:Nonlinear parametric models of viscoelastic fluid flows Download a PDF of the paper titled Nonlinear parametric models of viscoelastic fluid flows, by Cassio M. Oishi and Alan A. Kaptanoglu and J. Nathan Kutz and Steven L. Brunton Download PDF Abstract:Reduced-order models have been widely adopted in fluid mechanics, particularly in the context of Newtonian fluid flows.
[2307.11838] Data-Induced Interactions of Sparse Sensors
Large-dimensional empirical data in science and engineering frequently has low-rank structure and can be represented as a combination of just a few eigenmodes. Because of this structure, we can use just a few spatially localized sensor measurements to reconstruct the full state of a complex system.
PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator
arXiv:2306.12962 (eess) Download a PDF of the paper titled PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator, by Shaowu Pan and 4 other authors Download PDF Submission history From: Shaowu Pan [ view email] [v1] Thu, 22 Jun 2023 16:55:01 UTC (866 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Links to...
[2301.12649] Convergence of uncertainty estimates in Ensemble and Bayesian sparse model discovery
[Submitted on 30 Jan 2023 ( v1 ), last revised 26 Apr 2023 (this version, v2)] Title:Convergence of uncertainty estimates in Ensemble and Bayesian sparse model discovery Download a PDF of the paper titled Convergence of uncertainty estimates in Ensemble and Bayesian sparse model discovery, by L. Mars Gao and 3 other authors Download PDF Abstract: Sparse model identification enables nonlinear dynamical system discovery from data.
[2303.17078] Machine Learning for Partial Differential Equations
arXiv:2303.17078 (cs) Download a PDF of the paper titled Machine Learning for Partial Differential Equations, by Steven L. Brunton and J.
Saddle transport and chaos in the double pendulum - Nonlinear Dynamics
Abstract Pendulums are simple mechanical systems that have been studied for centuries and exhibit many aspects of modern dynamical systems theory. In particular, the double pendulum is a prototypical chaotic system that is frequently used to illustrate a variety of phenomena in nonlinear dynamics.
Chemical crystallography by serial femtosecond X-ray diffraction - Nature
Abstract Inorganic–organic hybrid materials represent a large share of newly reported structures, owing to their simple synthetic routes and customizable properties1. This proliferation has led to a characterization bottleneck: many hybrid materials are obligate microcrystals with low symmetry and severe radiation sensitivity, interfering with the standard techniques of single-crystal X-ray diffraction2,3 and electron microdiffraction4,5,6,7,8,9,10,11.
[2112.04307] Physics-informed dynamic mode decomposition (piDMD)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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