Paper Citation Record · LEDGER

Challenges in Training PINNs: A Loss Landscape Perspective

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 40 inbound Pith citation observations for arXiv:2402.01868.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2402.01868 v2

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measured 0 of 0 reference resolution

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measured 40 of 40 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 40 of 40 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:40:31.139865Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-02T15:47:06.293937Z

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Outbound references

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Pith citing papers

Observation c937c584-d684-4e49-ba1a-3f7b48255e27 · inbound

Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis cites this paper.

Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Challenges in Training PINNs: A Loss Landscape Perspective

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-12T21:03:39.703770Z

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Observation 0e7640fb-138c-427e-ac0f-416a3109283f · inbound

Visualizing Loss Functions as Topological Landscape Profiles cites this paper.

Visualizing Loss Functions as Topological Landscape Profiles Challenges in Training PINNs: A Loss Landscape Perspective

Reference 2012

Resolution: unresolved; no resolver link, observed 2026-08-12T17:56:29.090140Z

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Observation a9efc67e-97be-42d2-9a6c-613bda849de8 · inbound

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations cites this paper.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Challenges in Training PINNs: A Loss Landscape Perspective

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.371834Z

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Observation 72355189-f33e-45b7-a36b-2752d0ffdc04 · inbound

A Quasilinear Algorithm for Computing Higher-Order Derivatives of Deep Feed-Forward Neural Networks cites this paper.

A Quasilinear Algorithm for Computing Higher-Order Derivatives of Deep Feed-Forward Neural Networks Challenges in Training PINNs: A Loss Landscape Perspective

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T16:51:00.480088Z

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Observation 94805631-7077-44a9-8c44-894244b86ca1 · inbound

A Hybrid Virtual Element Method and Deep Learning Approach for Solving One-Dimensional Euler-Bernoulli Beams cites this paper.

A Hybrid Virtual Element Method and Deep Learning Approach for Solving One-Dimensional Euler-Bernoulli Beams Challenges in Training PINNs: A Loss Landscape Perspective

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T20:54:34.168706Z

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Observation 937367a0-04e6-4474-bd8b-b65fc5978e72 · inbound

PINNsAgent: Automated PDE Surrogation with Large Language Models cites this paper.

PINNsAgent: Automated PDE Surrogation with Large Language Models Challenges in Training PINNs: A Loss Landscape Perspective

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-10T17:38:24.578501Z

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Observation 9b28f786-59b8-4c7f-be47-32f69906d738 · inbound

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning cites this paper.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Challenges in Training PINNs: A Loss Landscape Perspective

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-10T13:56:23.255190Z

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Observation 00a4751b-dae3-48bd-81ae-3b9580ed4f00 · inbound

Learn Singularly Perturbed Solutions via Homotopy Dynamics cites this paper.

Learn Singularly Perturbed Solutions via Homotopy Dynamics Challenges in Training PINNs: A Loss Landscape Perspective

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-09T18:57:13.618083Z

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Observation c9a32f49-5f3b-4114-a414-7aa9dba61110 · inbound

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks cites this paper.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Challenges in Training PINNs: A Loss Landscape Perspective

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-09T17:45:05.761788Z

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Observation ce4e9284-37e8-476a-a41e-8278ea4f42e8 · inbound

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion cites this paper.

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion Challenges in Training PINNs: A Loss Landscape Perspective

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-09T14:24:43.369780Z

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Observation e17a822b-3c47-4dab-b38a-7784688d1d72 · inbound

MPFBench: A Large Scale Dataset for SciML of Multi-Phase-Flows: Droplet and Bubble Dynamics cites this paper.

MPFBench: A Large Scale Dataset for SciML of Multi-Phase-Flows: Droplet and Bubble Dynamics Challenges in Training PINNs: A Loss Landscape Perspective

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-08T13:59:42.434130Z

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Observation 5a7e6b0f-2d1f-4502-837f-2becf2c16bd2 · inbound

Integral regularization PINNs for evolution equations cites this paper.

Integral regularization PINNs for evolution equations Challenges in Training PINNs: A Loss Landscape Perspective

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-22T22:52:13.332787Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8b8c3459-3fbc-4994-bd9f-a87564ae448b · inbound

PINNs for Solving Unsteady Maxwell's Equations: Convergence Issues and Comparative Assessment with Compact Schemes cites this paper.

PINNs for Solving Unsteady Maxwell's Equations: Convergence Issues and Comparative Assessment with Compact Schemes Challenges in Training PINNs: A Loss Landscape Perspective

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T12:40:31.139865Z

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Observation 26399a6d-66f2-4ea6-bf65-a5087535fc90 · inbound

Weak Random Feature Method for Solving Partial Differential Equations cites this paper.

Weak Random Feature Method for Solving Partial Differential Equations Challenges in Training PINNs: A Loss Landscape Perspective

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-16T04:46:42.069792Z

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Observation e5d86202-685e-4157-b5c3-e7574c3a17a7 · inbound

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models cites this paper.

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models Challenges in Training PINNs: A Loss Landscape Perspective

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-16T00:00:01.718772Z

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Observation a2261146-729b-4bd4-ae23-897dc1bb682f · inbound

Enforced Interface Constraints for Domain Decomposition Method of Discrete Physics-Informed Neural Networks cites this paper.

Enforced Interface Constraints for Domain Decomposition Method of Discrete Physics-Informed Neural Networks Challenges in Training PINNs: A Loss Landscape Perspective

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-15T21:05:24.524362Z

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Observation 370834bf-0336-4092-84b5-0107e4e3c20f · inbound

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design cites this paper.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Challenges in Training PINNs: A Loss Landscape Perspective

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-15T22:48:55.050048Z

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Observation a4ef5262-c982-4d57-9de6-e8390c6ee1fa · inbound

Semi-Explicit Neural DAEs: Learning Long-Horizon Dynamical Systems with Algebraic Constraints cites this paper.

Semi-Explicit Neural DAEs: Learning Long-Horizon Dynamical Systems with Algebraic Constraints Challenges in Training PINNs: A Loss Landscape Perspective

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-07T14:01:27.087467Z

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Observation eb40ac2d-761d-47bf-92eb-577ee1ccb36c · inbound

SPINN: Advancing Cosmological Simulations of Fuzzy Dark Matter with Physics Informed Neural Networks cites this paper.

SPINN: Advancing Cosmological Simulations of Fuzzy Dark Matter with Physics Informed Neural Networks Challenges in Training PINNs: A Loss Landscape Perspective

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T11:15:58.021686Z

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Observation bc700e4e-6d4d-4d9d-9669-a600996f4374 · inbound

High precision PINNs in unbounded domains: application to singularity formulation in PDEs cites this paper.

High precision PINNs in unbounded domains: application to singularity formulation in PDEs Challenges in Training PINNs: A Loss Landscape Perspective

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-15T18:41:24.989766Z

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Observation 28b49c76-c61b-4d59-b653-ccf31c3cca1a · inbound

BWLer: Barycentric Weight Layer Elucidates a Precision-Conditioning Tradeoff for PINNs cites this paper.

BWLer: Barycentric Weight Layer Elucidates a Precision-Conditioning Tradeoff for PINNs Challenges in Training PINNs: A Loss Landscape Perspective

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T22:01:50.734585Z

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Observation 166dcdea-4742-41c5-b5e5-31647169243d · inbound

Physics-Informed Neural Networks with Hard Nonlinear Equality and Inequality Constraints cites this paper.

Physics-Informed Neural Networks with Hard Nonlinear Equality and Inequality Constraints Challenges in Training PINNs: A Loss Landscape Perspective

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T18:35:11.742139Z

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Observation e48237f0-31b8-490d-97bc-4de55f4f073b · inbound

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms cites this paper.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Challenges in Training PINNs: A Loss Landscape Perspective

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-05T14:49:00.401473Z

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Observation 6de7b2cb-75d2-4823-854f-55ae1ac49788 · inbound

Guaranteeing Conservation of Integrals with Projection in Physics-Informed Neural Networks cites this paper.

Guaranteeing Conservation of Integrals with Projection in Physics-Informed Neural Networks Challenges in Training PINNs: A Loss Landscape Perspective

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-03T22:46:59.139134Z

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Observation 106b98c3-305d-4b16-89cd-8a47d7afa9b7 · inbound

Optimizing Rank for High-Fidelity Implicit Neural Representations cites this paper.

Optimizing Rank for High-Fidelity Implicit Neural Representations Challenges in Training PINNs: A Loss Landscape Perspective

Reference 158

Resolution: unresolved; no resolver link, observed 2026-08-03T16:11:54.590086Z

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Observation 9a5cc3be-fcf6-40cd-8a9b-428dade46143 · inbound

SnareNet: Flexible Repair Layers for Neural Networks with Hard Constraints cites this paper.

SnareNet: Flexible Repair Layers for Neural Networks with Hard Constraints Challenges in Training PINNs: A Loss Landscape Perspective

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-16T03:37:13.677354Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4faa0b52-5cdc-46db-ac77-fb639cc8d765 · inbound

Adaptive Randomized Neural Networks with Locally Activation Function: Theory and Algorithm for Solving PDEs cites this paper.

Adaptive Randomized Neural Networks with Locally Activation Function: Theory and Algorithm for Solving PDEs Challenges in Training PINNs: A Loss Landscape Perspective

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:41:00.001042Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6adcc8e1-3d46-4bae-9807-2b97c33d1e0b · inbound

Material-agnostic temperature field prediction for metal additive manufacturing via a parametric PINN framework cites this paper.

Material-agnostic temperature field prediction for metal additive manufacturing via a parametric PINN framework Challenges in Training PINNs: A Loss Landscape Perspective

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-10T12:15:21.988844Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7b04dd6e-59fd-4e6a-9262-e5cecc0f58a4 · inbound

Material-agnostic temperature field prediction for metal additive manufacturing via a parametric PINN framework cites this paper.

Material-agnostic temperature field prediction for metal additive manufacturing via a parametric PINN framework Challenges in Training PINNs: A Loss Landscape Perspective

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-02T16:18:53.170621Z

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Observation 67c70818-2e81-4f6b-b71c-6c895f1a82fc · inbound

Physics-Informed Neural Networks for Solving Two-Flavor Neutrino Oscillations in Vacuum and Matter Environments for Atmospheric and Reactor Neutrinos cites this paper.

Physics-Informed Neural Networks for Solving Two-Flavor Neutrino Oscillations in Vacuum and Matter Environments for Atmospheric and Reactor Neutrinos Challenges in Training PINNs: A Loss Landscape Perspective

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:16:21.016233Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9ab21e90-edf9-4bf9-9ac0-d0e04b8f4a64 · inbound

Physics-Informed Neural Networks for Solving Two-Flavor Neutrino Oscillations in Vacuum and Matter Environments for Atmospheric and Reactor Neutrinos cites this paper.

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-14T22:18:04.049480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e5ac1cd6-07bb-4336-a53c-edd4b3761b81 · inbound

When PINNs Go Wrong: Pseudo-Time Stepping Against Spurious Solutions cites this paper.

When PINNs Go Wrong: Pseudo-Time Stepping Against Spurious Solutions Challenges in Training PINNs: A Loss Landscape Perspective

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:06:14.690098Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4170bdef-71f3-48f9-8250-d7c2d1c64919 · inbound

Physics informed operator learning of parameter dependent spectra cites this paper.

Physics informed operator learning of parameter dependent spectra Challenges in Training PINNs: A Loss Landscape Perspective

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:26:14.629486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ad3766dd-3c5b-40dd-a4ba-d90c2419df87 · inbound

AdamFLIP: Adaptive Momentum Feedback Linearization Optimization for Hard Constrained PINN Training cites this paper.

AdamFLIP: Adaptive Momentum Feedback Linearization Optimization for Hard Constrained PINN Training Challenges in Training PINNs: A Loss Landscape Perspective

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:41:23.906561Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ef28681c-614a-457f-a088-60bfc847ca14 · inbound

Curriculum Learning of Physics-Informed Neural Networks based on Spatial Correlation cites this paper.

Curriculum Learning of Physics-Informed Neural Networks based on Spatial Correlation Challenges in Training PINNs: A Loss Landscape Perspective

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-19T17:02:40.590095Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 12cbe5f6-04c1-48a8-b424-a08f9d55550b · inbound

Critical evaluation of PINN for FWD inverse analysis and differentiable FEM as an alternative cites this paper.

Critical evaluation of PINN for FWD inverse analysis and differentiable FEM as an alternative Challenges in Training PINNs: A Loss Landscape Perspective

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T05:16:39.537374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b1cbb9c9-5ba9-4ff5-8e9b-d5f756ae3d3a · inbound

Seed-Robust PINN Determination of $s$-Wave Bound States and Jost-Function-Based vertex constants in $_{\Lambda}^{208}$Pb cites this paper.

Seed-Robust PINN Determination of $s$-Wave Bound States and Jost-Function-Based vertex constants in $_{\Lambda}^{208}$Pb Challenges in Training PINNs: A Loss Landscape Perspective

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-07-02T15:47:06.295339Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 78e2a4d0-00d8-431c-b395-59835ea9326b · inbound

Effective Dimensionality as an Operator Invariant for Physics-Preserving Constraint Adaptation in Physics-Informed Neural Networks cites this paper.

Effective Dimensionality as an Operator Invariant for Physics-Preserving Constraint Adaptation in Physics-Informed Neural Networks Challenges in Training PINNs: A Loss Landscape Perspective

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-02T15:47:06.076890Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 53fa20cd-de12-4e62-b961-d5bfe196a34f · inbound

PIKS: Universal Physics-Informed Kernel Methods cites this paper.

PIKS: Universal Physics-Informed Kernel Methods Challenges in Training PINNs: A Loss Landscape Perspective

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-07-30T12:20:57.824150Z

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Observation c4c70068-6d3a-4aa1-9546-59c9cae23404 · inbound

Cosmo-SPINN: Fuzzy Dark Matter Simulations with Physics-Informed Generative Networks cites this paper.

Cosmo-SPINN: Fuzzy Dark Matter Simulations with Physics-Informed Generative Networks Challenges in Training PINNs: A Loss Landscape Perspective

Reference 67

Resolution: unresolved; no resolver link, observed 2026-07-31T02:27:16.990307Z

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