Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach | Paper Citation Record

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Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Unresolved cited work

Reference 1

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This paper cites Physics- informed machine learning.Nature Reviews Physics, 2021, 3: 422–440.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Physics- informed machine learning.Nature Reviews Physics, 2021, 3: 422–440

Reference 2

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Observation 993c591f-3ae2-4f8c-848d-436628d8ea08 · outbound

This paper cites Can physics-informed neural networks beat the finite element method?IMA Journal of Applied Mathematics, 2024, 89(1): 143–174.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Can physics-informed neural networks beat the finite element method?IMA Journal of Applied Mathematics, 2024, 89(1): 143–174

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:09.975529Z

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Observation 451f8247-5f35-46d8-9b7e-1e7c458e9cc5 · outbound

This paper cites Scientific ma- chine learning through physics-informed neural networks: Where we are and what’s next.Journal of Scientific Com- puting, 2022, 92(3): 88.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Scientific ma- chine learning through physics-informed neural networks: Where we are and what’s next.Journal of Scientific Com- puting, 2022, 92(3): 88

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:10.040610Z

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Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-07-07T20:34:10.047821Z

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Observation 1d5fc779-3b92-4af9-99c4-0f469aa4464e · outbound

This paper cites Review of physics-informed neural networks: Challenges in loss function design and geometric integration.Mathematics, 2025, 13(20): 3289.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Review of physics-informed neural networks: Challenges in loss function design and geometric integration.Mathematics, 2025, 13(20): 3289

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:10.044430Z

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Observation 79fcf393-3d0e-43d5-8e0d-006d10729418 · outbound

This paper cites A comprehensive review of theoreti- cal concepts and advancements in physics-informed neu- ral networks with applications in structural engineering.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach A comprehensive review of theoreti- cal concepts and advancements in physics-informed neu- ral networks with applications in structural engineering

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:09.960915Z

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Observation 18f7436e-4400-46f8-9c2d-9c71ef94aede · outbound

This paper cites Understanding and mitigat- ing gradient flow pathologies in physics-informed neural networks.SIAM Journal on Scientific Computing, 2021, 43(5): A3055–A3081.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Understanding and mitigat- ing gradient flow pathologies in physics-informed neural networks.SIAM Journal on Scientific Computing, 2021, 43(5): A3055–A3081

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:09.989386Z

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Observation fd8d3a3d-99d0-4a4a-b43e-fb4f1413a3cd · outbound

This paper cites When and why PINNs fail to train: A neural tangent kernel perspective.Journal of Computational Physics, 2022, 449: 110768.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach When and why PINNs fail to train: A neural tangent kernel perspective.Journal of Computational Physics, 2022, 449: 110768

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:10.107810Z

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Observation 2299e2c3-8411-44d3-9c16-92fbe30505fe · outbound

This paper cites On the convergence of physics informed neural networks for linear second- order elliptic and parabolic type PDEs.Communications in Computational Physics, 2020, 28(5): 2042–2074.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach On the convergence of physics informed neural networks for linear second- order elliptic and parabolic type PDEs.Communications in Computational Physics, 2020, 28(5): 2042–2074

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:10.092715Z

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Observation 61d6beec-f55f-4d85-9904-c18f375a5308 · outbound

This paper cites Characterizing possible failure modes in physics-informed neural networks.NeurIPS, 2021, 34: 26548–26560.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Characterizing possible failure modes in physics-informed neural networks.NeurIPS, 2021, 34: 26548–26560

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:10.005034Z

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Observation 22960dcc-c476-4e6d-a577-75036dc8c70a · outbound

This paper cites Challenges in training PINNs: A loss landscape perspective.ICML, 2024, 235: 42159–42191.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Challenges in training PINNs: A loss landscape perspective.ICML, 2024, 235: 42159–42191

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:10.036935Z

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Observation 09600aa9-4252-4cd7-9caf-f2d93d85c987 · outbound

This paper cites Gradient-enhanced physics- informed neural networks for forward and inverse PDE problems.CMAME, 2022, 393: 114823.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Gradient-enhanced physics- informed neural networks for forward and inverse PDE problems.CMAME, 2022, 393: 114823

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:10.081513Z

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Observation fbfcaad0-86b2-46d3-8e9d-b056b7e0fbe7 · outbound

This paper cites Self-adaptive physics- informed neural networks.Journal of Computational Physics, 2023, 474: 111722.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Self-adaptive physics- informed neural networks.Journal of Computational Physics, 2023, 474: 111722

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:10.002816Z

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Observation 1ad113d9-3caf-4914-91da-0261cb8b6529 · outbound

This paper cites Self-adaptive loss bal- anced physics-informed neural networks.Neurocomput- ing, 2022, 496: 11–34.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Self-adaptive loss bal- anced physics-informed neural networks.Neurocomput- ing, 2022, 496: 11–34

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:10.057868Z

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Observation 92f583eb-6e62-43f1-b306-7a06f90a7778 · outbound

This paper cites Extended physics-informed neural networks (XPINNs).Communi- cations in Computational Physics, 2020, 28(5): 2002– 2041.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Extended physics-informed neural networks (XPINNs).Communi- cations in Computational Physics, 2020, 28(5): 2002– 2041

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:09.976175Z

Source-reported events for the cited work

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Observation dce6a49c-4acb-4446-b6bd-57b3677eff76 · outbound

This paper cites Finite basis physics-informed neural networks (FBPINNs).Advances in Computational Mathematics, 2023, 49: 62.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Finite basis physics-informed neural networks (FBPINNs).Advances in Computational Mathematics, 2023, 49: 62

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:10.040271Z

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Observation 4ee406b2-e8e7-41a9-ad9d-9d9d9cc816da · outbound

This paper cites Fourier Domain Physics Informed Neural Network.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Fourier Domain Physics Informed Neural Network

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Resolution: verified exact; local_arxiv, observed 2026-07-07T20:34:09.786250Z

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Observation b82c657c-0b93-4aad-870d-07d7390b4b78 · outbound

This paper cites Enhanced physics-informed neural networks with augmented Lagrangian relaxation method.Neurocomputing, 2023, 548: 126424.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Enhanced physics-informed neural networks with augmented Lagrangian relaxation method.Neurocomputing, 2023, 548: 126424

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:10.029090Z

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Observation 81a688c9-05b2-4dc5-a107-86590cb46aae · outbound

This paper cites Self-adaptive weights based on balanced residual decay rate for PINNs and deep operator networks.Journal of Computational Physics, 2025, 542: 114226.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Self-adaptive weights based on balanced residual decay rate for PINNs and deep operator networks.Journal of Computational Physics, 2025, 542: 114226

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:09.956192Z

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Observation 98a67da9-1fdf-4198-8f30-5e1286b344c3 · outbound

This paper cites Loss- attentional physics-informed neural networks.Journal of Computational Physics, 2024, 501: 112781.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Loss- attentional physics-informed neural networks.Journal of Computational Physics, 2024, 501: 112781

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:10.059498Z

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Observation b18f8a5a-7379-4d05-a449-78684fff83b8 · outbound

This paper cites A survey on transfer learning.IEEE TKDE, 2010, 22(10): 1345–1359.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach A survey on transfer learning.IEEE TKDE, 2010, 22(10): 1345–1359

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:10.100876Z

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Observation a562035c-71ec-4300-b3ae-3391047226e9 · outbound

This paper cites Gradient-enhanced physics-informed neu- ral networks based on transfer learning for inverse prob- lems.Physica D, 2024, 459: 134023.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Gradient-enhanced physics-informed neu- ral networks based on transfer learning for inverse prob- lems.Physica D, 2024, 459: 134023

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:10.010724Z

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Observation f70a1a0e-efe8-4cb8-b372-5dd14a001854 · outbound

This paper cites Data-Guided Physics-Informed Neural Networks for Solving Inverse Problems in Partial Differential Equations.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Data-Guided Physics-Informed Neural Networks for Solving Inverse Problems in Partial Differential Equations

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Resolution: verified exact; local_arxiv, observed 2026-07-07T20:34:09.768891Z

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Observation 14e83914-4886-4887-984b-da081d4f7ada · outbound

This paper cites Transfer learn- ing for improved generalizability in causal PINNs for beam simulations.Engineering Applications of Artificial Intelligence, 2024, 133: 108085.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Transfer learn- ing for improved generalizability in causal PINNs for beam simulations.Engineering Applications of Artificial Intelligence, 2024, 133: 108085

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:10.045139Z

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Observation df08922a-3c37-4a36-8d3c-96d9a3da0cc1 · outbound

This paper cites A transfer learning- PINN (TL-PINN) for vortex-induced vibration.Ocean Engineering, 2022, 266: 113101.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach A transfer learning- PINN (TL-PINN) for vortex-induced vibration.Ocean Engineering, 2022, 266: 113101

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:10.054245Z

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Observation caa52e38-a834-48a4-9ad9-7ccb0fb4b80e · outbound

This paper cites Transfer Learning in Physics-Informed Neural Networks: Full Fine-Tuning, Lightweight Fine-Tuning, and Low-Rank Adaptation.Int J Mechanical System Dynamics, 2025, 5(2): 212–235.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Transfer Learning in Physics-Informed Neural Networks: Full Fine-Tuning, Lightweight Fine-Tuning, and Low-Rank Adaptation.Int J Mechanical System Dynamics, 2025, 5(2): 212–235

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:10.069829Z

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Observation 4d40ff58-8065-41f2-8687-176b83141dcc · outbound

This paper cites Adaptive trans- fer learning for PINN.Journal of Computational Physics, 2023, 490: 112291.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Adaptive trans- fer learning for PINN.Journal of Computational Physics, 2023, 490: 112291

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:10.078785Z

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Observation 6e30e2a4-545b-43bd-8e9c-82cf4ce28e0b · outbound

This paper cites How transferable are features in deep neural networks?NeurIPS, 2014, 27: 3320–3328.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach How transferable are features in deep neural networks?NeurIPS, 2014, 27: 3320–3328

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:10.083615Z

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Observation e0cc4d61-54f4-4198-aa83-7ffbb4817f4c · outbound

This paper cites Applications of physics-informed neural networks for property characterization of complex materials.RILEM Technical Letters, 2023, 7: 178–188.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Applications of physics-informed neural networks for property characterization of complex materials.RILEM Technical Letters, 2023, 7: 178–188

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:10.067709Z

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Observation 685af479-5b96-46f1-8b39-f698d3afc705 · outbound

This paper cites Physics-informed neural network for ul- trasound nondestructive quantification of surface break- ing cracks.Journal of Nondestructive Evaluation, 2020, 39(3): 61.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Physics-informed neural network for ul- trasound nondestructive quantification of surface break- ing cracks.Journal of Nondestructive Evaluation, 2020, 39(3): 61

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:09.983733Z

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Observation ef16477b-bad0-401e-8083-a2f83d98f182 · outbound

This paper cites Im- portance Estimation for Neural Network Pruning.CVPR, 2019: 11264–11272.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Im- portance Estimation for Neural Network Pruning.CVPR, 2019: 11264–11272

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:10.076230Z

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Observation 9974eede-064a-4299-add9-3b019538f9fe · outbound

This paper cites Learning both weights and connections for efficient neural networks.NeurIPS, 2015, 28: 1135–1143.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Learning both weights and connections for efficient neural networks.NeurIPS, 2015, 28: 1135–1143

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:09.955734Z

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Observation cc66b5a8-b83d-4653-8ae4-f8c39ad0f26a · outbound

This paper cites Deep Compression.ICLR, 2016.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Deep Compression.ICLR, 2016

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:10.048275Z

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Observation 5e9c0197-254c-4d76-9fdb-e7f4d074627e · outbound

This paper cites Unlearning Noise in PINNs: A Selective Pruning Framework for PDE Inverse Problems.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Unlearning Noise in PINNs: A Selective Pruning Framework for PDE Inverse Problems

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Resolution: verified exact; local_arxiv, observed 2026-07-07T20:34:09.775827Z

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Observation 4595b0b6-2165-4a84-ad03-28c0f040236f · outbound

This paper cites The Lottery Ticket Hypothesis.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach The Lottery Ticket Hypothesis

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:10.036581Z

Source-reported events for the cited work

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Observation 1b6ddcea-e8fd-4fb9-8141-fd88676e6b9a · outbound

This paper cites Understanding the difficulty of train- ing deep feedforward neural networks.AISTATS, 2010, PMLR 9: 249–256.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Understanding the difficulty of train- ing deep feedforward neural networks.AISTATS, 2010, PMLR 9: 249–256

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:10.073552Z

Source-reported events for the cited work

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Observation 9ebb9308-bc8a-4366-a5ba-2b4ef2275163 · outbound

This paper cites Batch Normalization.ICML, 2015, PMLR 37: 448–456.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Batch Normalization.ICML, 2015, PMLR 37: 448–456

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:10.088074Z

Source-reported events for the cited work

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Observation 31b28e11-3675-4e04-b7c5-586d274f179d · outbound

This paper cites Layer Normalization.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Layer Normalization

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Resolution: verified exact; local_arxiv, observed 2026-07-07T20:34:09.781752Z

Source-reported events for the cited work

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Observation 2f0458ed-c472-4fd4-ba71-aaf1e42f11d6 · outbound

This paper cites Maximum likeli- hood from incomplete data via the EM algorithm.JRSS B, 1977, 39(1): 1–38.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Maximum likeli- hood from incomplete data via the EM algorithm.JRSS B, 1977, 39(1): 1–38

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:10.097567Z

Source-reported events for the cited work

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Observation 4da0d15c-69be-4026-ac00-942efacb63b5 · outbound

This paper cites On a measure of divergence between two statistical populations.Bulletin of the Calcutta Math- ematical Society, 1943, 35: 99–109.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach On a measure of divergence between two statistical populations.Bulletin of the Calcutta Math- ematical Society, 1943, 35: 99–109

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:10.019337Z

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Observation 42a1d276-d425-42e9-b21e-e549988e1d29 · outbound

This paper cites Estimating the dimension of a model.The An- nals of Statistics, 1978, 6(2): 461–464.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Estimating the dimension of a model.The An- nals of Statistics, 1978, 6(2): 461–464

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:10.079081Z

Source-reported events for the cited work

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Observation f44a8737-0456-4464-8428-eeae6b6cb01a · outbound

This paper cites Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:10.017156Z

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Observation d3e14613-26f8-490c-97d1-9954ce401f9b · outbound

This paper cites Soft Thresh- old Weight Reparameterization for Learnable Sparsity.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Soft Thresh- old Weight Reparameterization for Learnable Sparsity

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:10.032810Z

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This paper cites Movement Pruning: Adap- tive Sparsity by Fine-Tuning.NeurIPS, 2020, 33: 20378– 20389.

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach Movement Pruning: Adap- tive Sparsity by Fine-Tuning.NeurIPS, 2020, 33: 20378– 20389

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T20:34:10.064464Z

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