{
  "abstract": "Fast prediction of the response of adhesive soft viscoelastic contacts represents a current challenge in soft robotics and for gripping and manipulation tasks. Determining the complete time-resolved force trajectory requires full numerical simulations, whose computational cost is strongly parameter-dependent, making them impractical for real-time application or design-optimization loops. In this work, we overcome this limitation by training a scalar-conditioned, stateful, sequence-to-sequence deep learning model to predict the full force evolution from a prescribed displacement history for both short- and long-range adhesion regimes. The data set spans four orders of magnitude in loading and unloading rates and includes varied dwell times, with the Tabor parameter ranging from $0.2$ to $3.2$. To enable learning across these heterogeneous time scales, we introduce a fixed-measurement-step (FMS) representation that converts variable-length trajectories into fixed-length sequences while preserving their physical-time information. Different architectures were trained, including long short-term memory (LSTM) networks, temporal convolutional neural (TCN) networks, and time-distributed dense layers with three different Tabor-conditioning mechanisms. The models were compared using global waveform and error metrics. We found that the best-performing model has an LSTM architecture with concatenated conditioning, which achieves a held-out mean-squared error of $5.0\\times10^{-4}$, a median pull-off-force error of $\\approx2.2\\%$, and a median hysteresis error of $\\approx1.1\\%$. For the held-out protocols, the model predicts a complete force trajectory with a median inference time of $0.16$ s. The model is tested across unseen parameter combinations and against analytical limiting cases, providing a rapid surrogate for repeated numerical evaluations with potential use in control-oriented applications.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2607.19060",
  "author_total": 4,
  "authors": [
    {
      "name": "Ali Maghami"
    },
    {
      "name": "Merten Stender"
    },
    {
      "name": "Michele Ciavarella"
    },
    {
      "name": "Antonio Papangelo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f6j89xmz5d5rx.bib",
  "cited_by": 1,
  "doi": "10.48550/arxiv.2607.19060",
  "fulltext_url": null,
  "id": "f6j89xmz5d5rx",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2607.19060",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.soft"
    ],
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "physics.data-an"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "2211|Adhesion, Friction, and Surface Interactions"
    ]
  ],
  "openalex": "W7170040527",
  "pdf_url": "https://arxiv.org/pdf/2607.19060",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/f6j89xmz5d5rx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:45Z",
      "native_id": "2607.19060",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.19060"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    },
    {
      "field": "physics",
      "origin": "arxiv:physics.data-an",
      "subfield": "computational"
    },
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.soft",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Deep learning-based prediction of time-resolved adhesive forces in viscoelastic Hertzian contacts",
  "title_text": "Deep learning-based prediction of time-resolved adhesive forces in viscoelastic Hertzian contacts",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:56Z",
  "url": "https://research.draconitic.com/paper/f6j89xmz5d5rx",
  "venue": "arXiv",
  "versions": []
}