Pith Number

pith:PV36GWPC

pith:2026:PV36GWPCU3CCH2UT44RAUOJHAY

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Topological Data Analysis combined with Machine Learning for Predicting Permeability of Porous Media

Aakash Karlekar, Catherin Neena Lalu, Ebru Dagdelen, Jonathan Jaquette, Linda Cummings, Lou Kondic, Manav Arora, Matthew Illingworth

Topological data analysis supplies effective features for machine learning models that predict permeability in porous media from structure.

arxiv:2605.17581 v1 · 2026-05-17 · cond-mat.soft · cs.LG

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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

We show, in particular, that topological data analysis (TDA) provides a useful set of features that can be easily combined with ML to yield meaningful results.

C2weakest assumption

The assumption that features extracted from synthetic porous media (structural, topological, and network measures) are sufficient to train an ML model that generalizes to predict permeability in a way that captures the underlying physics rather than just fitting the training set.

C3one line summary

TDA-derived topological features combined with standard ML algorithms predict permeability of synthetic porous media using exact ground-truth values for training and validation.

References

300 extracted · 300 resolved · 0 Pith anchors

[1] Sanaei, P. and Richardson, G. W. and Witelski, T. and Cummings, L. J. , title =. J. Fluid Mech. , volume =. 2016 , doi = 2016

[2] Edelsbrunner, H. and M. Three-dimensional alpha shapes , journal =. 1994 , doi = 1994

[3] and others , title = 2019

[4] and others , title = 2016

[5] and others , title =

Formal links

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Receipt and verification

First computed	2026-05-20T00:04:47.122117Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

7d77e359e2a6c423ea93e7220a39270626f4da636dea2ec1b737b18a069b8b0c

Aliases

arxiv: 2605.17581 · arxiv_version: 2605.17581v1 · doi: 10.48550/arxiv.2605.17581 · pith_short_12: PV36GWPCU3CC · pith_short_16: PV36GWPCU3CCH2UT · pith_short_8: PV36GWPC

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/PV36GWPCU3CCH2UT44RAUOJHAY \
  | 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: 7d77e359e2a6c423ea93e7220a39270626f4da636dea2ec1b737b18a069b8b0c

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "f1445e84bb15b58221b263e465983683b765f9ac098811fb571330f7ebd58ea0",
    "cross_cats_sorted": [
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cond-mat.soft",
    "submitted_at": "2026-05-17T18:19:20Z",
    "title_canon_sha256": "eb611764e262ed7c0341e4cd82811dec3e43ec0f71d2efa1c4c93f60e3297964"
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  "source": {
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    "kind": "arxiv",
    "version": 1
  }
}