Pith Number

pith:3YJW5CAH

pith:2026:3YJW5CAHMD4E3HIMHVJ36464XF

not attested not anchored not stored refs resolved

DiffPhD: A Unified Differentiable Solver for Projective Heterogeneous Materials in Elastodynamics with Contact-Rich GPU-Acceleration

Bing-Yu Chen, Fan Shi, Jui-I Huang, Peter Yichen Chen, Shih-Yu Lai, Siyuan Luo, Sung-Han Tien, Tiantian Liu, Yen-Chen Tseng, Yi-Ting Chiu, Yu-Lun Liu, Ziqiu Zeng

DiffPhD unifies stiffness-aware projective weights, trust-region filtering, and Anderson acceleration into a single GPU pipeline that delivers exact gradients and up to 10x speedup on heterogeneous hyperelastic contact simulations.

arxiv:2605.14526 v1 · 2026-05-14 · cs.GR · cs.DC · cs.NA · cs.RO · math.NA

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

DiffPhD achieves strict gradient accuracy while delivering up to an order-of-magnitude speedup over prior differentiable solvers on heterogeneous, hyperelastic, contact-rich benchmarks. Crucially, this speedup does not come at the cost of stability: DiffPhD remains convergent on stiffness contrasts up to 100x where prior PD solvers degrade.

C2weakest assumption

The trust-region eigenvalue filtering and type-II Anderson acceleration with dual-gate convergence preserve both forward stability and exact gradient accuracy across all tested stiffness contrasts and contact configurations without requiring case-by-case retuning or introducing hidden bias in the backward pass.

C3one line summary

DiffPhD delivers a unified differentiable projective dynamics solver for heterogeneous hyperelastic elastodynamics with contact that achieves up to 10x speedup and stable convergence on 100x stiffness contrasts while preserving strict gradient accuracy.

References

145 extracted · 145 resolved · 2 Pith anchors

[1] The Computational Geometry Algorithms Library , author =

[2] Menelaos Karavelas , subtitle =

[3] The Computational Geometry Algorithms Library , subtitle =

[4] The Parmap library , author =

[5] Christopher Anderson and Sophia Drossopoulou , title =

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-17T23:39:06.003898Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

de136e880760f84d9d0c3d53bf73dcb97e4c281b6d6c6e773b1f9082f4765b1b

Aliases

arxiv: 2605.14526 · arxiv_version: 2605.14526v1 · doi: 10.48550/arxiv.2605.14526 · pith_short_12: 3YJW5CAHMD4E · pith_short_16: 3YJW5CAHMD4E3HIM · pith_short_8: 3YJW5CAH

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/3YJW5CAHMD4E3HIMHVJ36464XF \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: de136e880760f84d9d0c3d53bf73dcb97e4c281b6d6c6e773b1f9082f4765b1b

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "e1fdb9cf3cff9ab3bf1407db1954997e3a43647ad869641c79c238cce4065a0a",
    "cross_cats_sorted": [
      "cs.DC",
      "cs.NA",
      "cs.RO",
      "math.NA"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "primary_cat": "cs.GR",
    "submitted_at": "2026-05-14T08:09:42Z",
    "title_canon_sha256": "26d4d22679463fe58bb7e17b15d600af0e67350c83a4d558ad4118235dd784ce"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.14526",
    "kind": "arxiv",
    "version": 1
  }
}