Pith Number

pith:IWZJV5DM

pith:2026:IWZJV5DME2U37MZXR2OIKC5WBP

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Physics Informed Neural Network-based Computational Method for Accelerating Time-Periodic Unsteady CFD Simulations

Atul Sharma, Harshita Agarwal, Lakshya Chaplot

Physics-informed neural network computes time-periodic flows by training over one period instead of simulating transients.

arxiv:2605.18340 v1 · 2026-05-18 · physics.comp-ph · physics.flu-dyn

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Claims

C1strongest claim

Our results demonstrate that the PINN-based periodic solver takes substantially less computational time to achieve almost same accuracy as that obtained by the traditional transient-to-periodic solver.

C2weakest assumption

That a neural network optimized solely over one time period using a physics-informed loss can converge to the correct periodic solution without any dependence on or simulation of the transient evolution from initial conditions.

C3one line summary

A PINN-based periodic CFD solver is shown to reach nearly the same accuracy as traditional transient-to-periodic methods but with substantially lower computational time for 2D heat diffusion and fluid flow cases.

References

46 extracted · 46 resolved · 1 Pith anchors

[1] T. Colonius, D. R. Williams, Control of vortex shedding on two- and three-dimensional aerofoils, Philosophical transactions. Series A, Mathematical, physical, and engineering sciences 369 (2011) 1525– 2011 · doi:10.1098/rsta.2010.0355

[2] S. R. Morab, J. S. Murallidharan, A. Sharma, Computational hemodynamics and hemoacoustic study on carotid bifurcation: Effect of stenosis and branch angle, Physics of Fluids 36 (4 2024). doi:10.1063/5 2024 · doi:10.1063/5.0203193/3281081

[3] M. Mehrabi, S. Setayeshi, Computational fluid dynamics analysis of pulsatile blood flow behavior in modelled stenosed vessels with different severities, Mathematical Problems in Engineering 2012 (2012 2012 · doi:10.1155/2012/804765

[4] In: 2018 AIAA Atmospheric Flight Mechanics Conference 2001 · doi:10.2514/6

[5] Y. Cao, L. Zhou, C. Ou, H. Fang, D. Liu, 3d cfd simulation and analysis of transient flow in a water pipeline, Aqua Water Infrastructure, Ecosystems and Society 71 (2022) 751–767. doi:10.2166/AQUA.202 2022 · doi:10.2166/aqua.2022.023

Receipt and verification

First computed	2026-05-20T00:05:55.970857Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

45b29af46c26a9bfb3378e9c850bb60bf7569f5ca1a6aa00653da680957cf6d7

Aliases

arxiv: 2605.18340 · arxiv_version: 2605.18340v1 · doi: 10.48550/arxiv.2605.18340 · pith_short_12: IWZJV5DME2U3 · pith_short_16: IWZJV5DME2U37MZX · pith_short_8: IWZJV5DM

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/IWZJV5DME2U37MZXR2OIKC5WBP \
  | 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: 45b29af46c26a9bfb3378e9c850bb60bf7569f5ca1a6aa00653da680957cf6d7

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "eb30467a8dcb464f91dc3ab654ff7e1198603b8f2add39544e4b498761f50fdf",
    "cross_cats_sorted": [
      "physics.flu-dyn"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "physics.comp-ph",
    "submitted_at": "2026-05-18T12:54:23Z",
    "title_canon_sha256": "ba13e3fb7e68dde270a023f278144ca0860da454a37dfa7e4f74ace2b6514642"
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  "source": {
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    "kind": "arxiv",
    "version": 1
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}