{
  "annotation_id": "03aa5747-0122-4f9d-ae92-c07842b58420",
  "date_created": "2026-02-17T05:53:05.001000Z",
  "date_modified": "2026-02-17T05:53:05.001000Z",
  "file_hash": "2547c539ab24f4543136e64d4835f68722c87e2216bb000fef26bac5c97305a7",
  "private": false,
  "record": {
    "abstract": "We present JAX-PF, an open-source, GPU-accelerated, and differentiable Phase Field (PF) software package, supporting both explicit and implicit time stepping schemes. Leveraging the modern computing architecture JAX, JAX-PF achieves high performance through array programming and GPU acceleration, delivering ~5x speedup over PRISMS-PF with MPI (24 CPU cores) for systems with ~4.19 million degrees of freedom using explicit schemes, and scaling efficiently with implicit schemes for large-size problems. Furthermore, a key feature of JAX-PF is automatic differentiation (AD), eliminating manual derivations of free-energy functionals and Jacobians. Beyond forward simulations, JAX-PF demonstrates its potential in inverse design by providing sensitivities for gradient-based optimization. We demonstrate, for the first time, the calibration of PF material parameters using AD-based sensitivities, highlighting its capability for high-dimensional inverse problems. By combining efficiency, flexibility, and full differentiability, JAX-PF offers a fast, practical, and integrated tool for forward simulation and inverse design, advancing co-designing of material and manufacturing processes and supporting the goals of the Materials Genome Initiative.",
    "arxiv_id": "2601.06079",
    "authors": [
      "Fanglei Hu",
      "Jiachen Guo",
      "Stephen Niezgoda",
      "Wing Kam Liu",
      "Jian Cao"
    ],
    "categories": [
      "physics.comp-ph",
      "cs.CE"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "title": "Efficient GPU-computing simulation platform JAX-PF for differentiable phase field model",
    "url": "https://arxiv.org/abs/2601.06079",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "fa16deef-962d-4893-855c-c28ae0491863",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}