{
  "annotation_id": "d697eff5-5641-4f47-b747-b96485706768",
  "date_created": "2026-02-17T05:53:08.666000Z",
  "date_modified": "2026-02-17T05:53:08.666000Z",
  "file_hash": "fa40b8efaff3f822ad902f16748e36cc844f8f727e72b8d1e007c7b2dae015c2",
  "private": false,
  "record": {
    "abstract": "In this work, we embed hard constraints in a physics informed neural network (PINN) which predicts solutions to the 2D incompressible Navier Stokes equations. We extend the hard constraint method introduced by Chen et al. (arXiv:2012.06148) from a linear PDE to a strongly non-linear PDE. The PINN is used to estimate the stream function and pressure of the fluid, and by differentiating the stream function we can recover an incompressible velocity field. An unlearnable hard constraint projection (HCP) layer projects the predicted velocity and pressure to a hyperplane that admits only exact solutions to a discretised form of the governing equations.",
    "arxiv_id": "2601.06244",
    "authors": [
      "Miranda J. S. Horne",
      "Peter K. Jimack",
      "Amirul Khan",
      "He Wang"
    ],
    "categories": [
      "physics.flu-dyn",
      "cs.LG"
    ],
    "doi": "10.34734/FZJ-2025-02453",
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Hard Constraint Projection in a Physics Informed Neural Network",
    "url": "https://arxiv.org/abs/2601.06244",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "45039fa6-513b-412f-bda9-3acd8ebdd8f5",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}