AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。
[Submitted on 12 Jun 2026] Title:Fundamental Dynamical Units for Physics-Informed Structural Inference from Perturbation Time-Series in Networked Systems View a PDF of the paper titled Fundamental Dynamical Units for Physics-Informed Structural Inference from Perturbation Time-Series in Networked Systems, by Nima Nouri View PDF HTML (experimental) Abstract:In networked dynamical systems, the parameter of primary mechanistic interest is signed interaction structure. Recovering this structure from perturbation time-series data is a fundamental identification problem, compounded by three coupled obstacles: the combinatorial complexity of interaction architectures, ambiguity of causal attribution under limited interventions, and state-dependent dynamics that confound structural inference. Each obstacle is structural in origin and calls for a structural solution. We address these challenges by adopting a reductionist approach, introducing Fundamental Dynamical Units (FDUs): signed three-node interaction patterns as composable primitives that convert the interaction hypothesis space into a finite, constructive, and tractable representation. We show that local interaction structure determines the perturbation conditions required to disentangle direct from relayed influence, making intervention design a structural consequence of the FDU representation. We embed FDU-regularized structural inference within a physics-informed neural ordinary differential equation (ODE) whose governing-equation constraint transforms structural hypotheses into verifiable dynamical predictions, enabling joint recovery of interaction structure and perturbation-resolved trajectories. Validated on synthetic benchmarks with known ground truth, the framework supports structural commitment, expressed through FDU primitives, motif-prescribed intervention design, and physics-informed learning, as a principled basis for mechanistically interpretable inference in networked dynamical systems. Subjects: Machine Learning (cs.LG); Computational Physics (physics.comp-ph) Cite as: arXiv:2609.11934 [cs.LG] (or arXiv:2609.11934v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.11934 arXiv-issued DOI via DataCite Submission history From: Nima Nouri [view email] [v1] Fri, 12 Jun 2026 11:58:37 UTC (6,424 KB) Full-text links: Access Paper: View a PDF of the paper titled Fundamental Dynamical Units for Physics-Informed Structural Inference from Perturbation Time-Series in Networked Systems, by Nima Nouri View PDF HTML (experimental) TeX Source view license Ancillary-file links: Ancillary files (details): supplementary_information.pdf supplementary_material.xlsx Current browse context: cs.LG new | recent | 2026-09 Change to browse by: cs physics physics.comp-ph References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) IArxiv recommender toggle IArxiv Recommender (What is IArxiv?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators 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. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)