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pith:UI7PP7J5

pith:2025:UI7PP7J5RF7JCVLQJTU6SHZPQQ

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LNN-PINN: A Unified Physics-Only Training Framework with Liquid Residual Blocks

Fujun Liu, Hanxuan Wang, Ze Tao

Adding liquid residual gating inside hidden layers improves PINN accuracy on benchmarks while keeping training unchanged.

arxiv:2508.08935 v4 · 2025-08-12 · cs.LG · cs.AI

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Claims

C1strongest claim

Across four benchmark problems, LNN-PINN consistently reduced RMSE and MAE under identical training conditions, with absolute error plots further confirming its accuracy gains.

C2weakest assumption

The observed accuracy improvements arise solely from the architectural addition of the liquid residual gating mechanism inside the hidden-layer mapping and not from any unintended change in effective capacity, optimization dynamics, or data handling.

C3one line summary

LNN-PINN integrates liquid residual blocks into PINNs and reports lower RMSE and MAE on four benchmark problems while leaving the original physics modeling and optimization pipeline unchanged.

References

30 extracted · 30 resolved · 0 Pith anchors

[1] Physics- informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations 2019

[2] Physics-informed neuralnetworksforpdeproblems:acomprehensivereview 2025

[3] Physics-informed neural networks(pinn)forcomputationalsolidmechanics:Numericalframe- works and applications.Thin-Walled Structures, 205:112495, 2024 2024

[4] Analytical and neural network approaches for solving two-dimensional nonlinear transient heat conduction 2025

[5] Physics- informed neural networks with adaptive localized artificial viscosity 2023

Cited by

3 papers in Pith

From Simple to Complex: Curriculum-Guided Physics-Informed Neural Networks via Gaussian Mixture Models

LSTM-PINN for Steady-State Electrothermal Transport: Preserving Multi-Field Consis tency in Strongly Coupled Heat and Fluid Flow

High-Fidelity Reconstruction of Charge Boundary Layers and Sharp Interfaces in Electro-Thermal-Convective Flows via Residual-Attention PINNs

Receipt and verification

First computed	2026-05-28T02:04:42.277824Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

a23ef7fd3d897e9155704ce9e91f2f843ba6480d9d444faea7f839aabe9adc11

Aliases

arxiv: 2508.08935 · arxiv_version: 2508.08935v4 · doi: 10.48550/arxiv.2508.08935 · pith_short_12: UI7PP7J5RF7J · pith_short_16: UI7PP7J5RF7JCVLQ · pith_short_8: UI7PP7J5

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/UI7PP7J5RF7JCVLQJTU6SHZPQQ \
  | 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: a23ef7fd3d897e9155704ce9e91f2f843ba6480d9d444faea7f839aabe9adc11

Canonical record JSON

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    "submitted_at": "2025-08-12T13:35:46Z",
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