{
  "annotation_id": "d1ad2aca-a1d7-4e0e-a358-e646c0298b61",
  "date_created": "2026-03-02T18:01:07.418000Z",
  "date_modified": "2026-03-02T18:01:07.418000Z",
  "file_hash": "d5dc7d54c35547ac6fac8bf029e2afbd45158ec935b33b4b7f759f602f872bda",
  "private": false,
  "record": {
    "abstract": "An effective modeling method for nonlinear distributed parameter systems\n(DPSs) is critical for both physical system analysis and industrial\nengineering. In this Rapid Communication, we propose a novel DPS modeling\napproach, in which a high-order nonlinear Volterra series is used to separate\nthe time/space variables. With almost no additional computational complexity,\nthe modeling accuracy is improved more than 20 times in average comparing with\nthe traditional method.",
    "arxiv_id": "physics/0605136",
    "authors": [
      "Hai-Tao Zhang",
      "Chen-Kun Qi",
      "Tao Zhou",
      "Han-Xiong Li"
    ],
    "categories": [
      "physics.data-an",
      "physics.flu-dyn"
    ],
    "doi": "10.1016/j.physa.2006.10.014",
    "journal_ref": "Physica A 376, 215-222(2007)",
    "title": "Greatly enhancing the modeling accuracy for distributed parameter   systems by nonlinear time/space separation",
    "url": "https://arxiv.org/abs/physics/0605136"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "4b6516b3-62e2-4cce-bd69-ff1decd11a35",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}