{
  "annotation_id": "31a263af-6ca2-4f02-9e77-ebae60d510a2",
  "date_created": "2026-03-02T18:00:36.297000Z",
  "date_modified": "2026-03-02T18:00:36.297000Z",
  "file_hash": "caa87950778a912f28473468c177c631bba15bb3866da3879d415c4f64435302",
  "private": false,
  "record": {
    "abstract": "A novel algorithm was recently presented to utilize emerging time dependent\nprobability density data to extract molecular potential energy surfaces. This\npaper builds on the previous work and seeks to enhance the capabilities of the\nextraction algorithm: An improved method of removing the generally ill-posed\nnature of the inverse problem is introduced via an extended Tikhonov\nregularization and methods for choosing the optimal regularization parameters\nare discussed. Several ways to incorporate multiple data sets are investigated,\nincluding the means to optimally combine data from many experiments exploring\ndifferent portions of the potential. Results are presented on the stability of\nthe inversion procedure, including the optimal combination scheme, under the\ninfluence of data noise. The method is applied to the simulated inversion of a\ndouble well system.",
    "arxiv_id": "physics/0109043",
    "authors": [
      "Lukas Kurtz",
      "Herschel Rabitz",
      "Regina de Vivie-Riedle"
    ],
    "categories": [
      "physics.chem-ph",
      "physics.gen-ph"
    ],
    "doi": "10.1103/PhysRevA.65.032514",
    "journal_ref": "Phys. Rev. A 65 (2002) 032514",
    "title": "Optimal use of time dependent probability density data to extract   potential energy surfaces",
    "url": "https://arxiv.org/abs/physics/0109043"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "a1c4e51d-9ffa-4fe4-a429-e390405b803f",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}