Paper Citation Record · LEDGER

Interpretability and Generalization Bounds for Learning Spatial Physics

As of 19 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2506.15199.

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

pith.paper-citation-record.v1
2506.15199 v4

Coverage vector

measured 38 of 38 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-15T19:52:08.688517Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

38 of 38 outbound references displayed

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External citation measurements

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Outbound references

Observation d4cd07fd-f44a-4e8d-b2d7-902a88b91b6d · outbound

This paper cites On the optimization of deep networks: Implicit acceleration by overparameterization.

Interpretability and Generalization Bounds for Learning Spatial Physics On the optimization of deep networks: Implicit acceleration by overparameterization

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:52:09.268359Z

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Observation 306e2d56-12c9-4cee-b259-7b8abf08b6c5 · outbound

This paper cites Baratta, Joseph P.

Interpretability and Generalization Bounds for Learning Spatial Physics Baratta, Joseph P

Reference 2

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Observation d62a9e41-9060-41bb-b5cf-cd40df6e1d84 · outbound

This paper cites Data-driven discovery of green’s functions with human-understandable deep learning.

Interpretability and Generalization Bounds for Learning Spatial Physics Data-driven discovery of green’s functions with human-understandable deep learning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:52:09.255163Z

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Observation f66ca838-3989-4210-8980-a13e71a492e3 · outbound

This paper cites Elliptic pde learning is provably data-efficient.

Interpretability and Generalization Bounds for Learning Spatial Physics Elliptic pde learning is provably data-efficient

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:52:09.241858Z

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Observation 1d42fc69-8e95-485a-bb19-ee3f8a98fcbf · outbound

This paper cites Choose a transformer: Fourier or galerkin.

Interpretability and Generalization Bounds for Learning Spatial Physics Choose a transformer: Fourier or galerkin

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:52:09.228802Z

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Observation cabe6efb-a136-43fa-89b2-a02f05e31cc0 · outbound

This paper cites The finite element method for elliptic problems.

Interpretability and Generalization Bounds for Learning Spatial Physics The finite element method for elliptic problems

Reference 6

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Observation 915d80d4-856d-4210-8b08-aa4b52ac7ba6 · outbound

This paper cites Kutz, and Steven Brunton.

Interpretability and Generalization Bounds for Learning Spatial Physics Kutz, and Steven Brunton

Reference 7

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Observation 4c921182-f161-45a7-a441-583fafe0d2f5 · outbound

This paper cites Deepgreen: deep learning of green’s functions for nonlinear boundary value problems.

Interpretability and Generalization Bounds for Learning Spatial Physics Deepgreen: deep learning of green’s functions for nonlinear boundary value problems

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:52:09.206786Z

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Observation 9092d167-fe71-4a4e-933f-3008efa8abf9 · outbound

This paper cites Learning to Drive from a World Model.

Interpretability and Generalization Bounds for Learning Spatial Physics Learning to Drive from a World Model

Reference 9

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Observation 74ff7fcb-226d-4805-b057-c1cc6f8a9efc · outbound

This paper cites Recurrent world models facilitate policy evolution.

Interpretability and Generalization Bounds for Learning Spatial Physics Recurrent world models facilitate policy evolution

Reference 10

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Observation 69ec81bd-5a54-4b2d-bee5-9f6213640d5e · outbound

This paper cites Learning physical models that can respect conservation laws.

Interpretability and Generalization Bounds for Learning Spatial Physics Learning physical models that can respect conservation laws

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:52:09.185419Z

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Observation a61c5f7d-d78d-40fe-8ce4-9f71555878a1 · outbound

This paper cites Maddix, Shima Alizadeh, Gaurav Gupta, and Michael W.

Interpretability and Generalization Bounds for Learning Spatial Physics Maddix, Shima Alizadeh, Gaurav Gupta, and Michael W

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:52:09.172200Z

Source-reported events for the cited work

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Observation 97df1f38-ea70-4ba8-8bef-8efa9df5601e · outbound

This paper cites Kaptanoglu, Brian M.

Interpretability and Generalization Bounds for Learning Spatial Physics Kaptanoglu, Brian M

Reference 13

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Observation b3b767b6-6e1f-46cd-81d8-8698858ceb25 · outbound

This paper cites A library for learning neural operators, 2024.

Interpretability and Generalization Bounds for Learning Spatial Physics A library for learning neural operators, 2024

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:52:09.159001Z

Source-reported events for the cited work

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Observation d656cb1b-b07d-4310-82e4-59a8b3ea925f · outbound

This paper cites Neural Operator: Learning Maps Between Function Spaces.

Interpretability and Generalization Bounds for Learning Spatial Physics Neural Operator: Learning Maps Between Function Spaces

Reference 15

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Observation 27a01623-e5cb-4674-ac35-ac23498313fd · outbound

This paper cites Characterizing possible failure modes in physics-informed neural networks.

Interpretability and Generalization Bounds for Learning Spatial Physics Characterizing possible failure modes in physics-informed neural networks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T19:52:08.600657Z

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Observation a294ec4f-fbb4-4c55-a2e6-a248f8d01690 · outbound

This paper cites Learning continuous models for continuous physics.

Interpretability and Generalization Bounds for Learning Spatial Physics Learning continuous models for continuous physics

Reference 17

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Observation 7c28ac79-6397-4d80-aba5-8c54b835425d · outbound

This paper cites Finite difference methods for ordinary and partial differential equations: steady-state and time-dependent problems.

Interpretability and Generalization Bounds for Learning Spatial Physics Finite difference methods for ordinary and partial differential equations: steady-state and time-dependent problems

Reference 18

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Observation 72e619b4-1192-4e82-a47e-5b9d2ddf230f · outbound

This paper cites Transformers Handle Endogeneity in In-Context Linear Regression.

Interpretability and Generalization Bounds for Learning Spatial Physics Transformers Handle Endogeneity in In-Context Linear Regression

Reference 19

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Observation d4a374c4-a0f6-4c00-b673-b0a8b53d9d77 · outbound

This paper cites DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators.

Interpretability and Generalization Bounds for Learning Spatial Physics DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators

Reference 20

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Observation 19da331b-8610-4dfa-8ca0-84b7e1a3ab17 · outbound

This paper cites Learning nonlinear operators via deeponet based on the universal approximation theorem of operators.

Interpretability and Generalization Bounds for Learning Spatial Physics Learning nonlinear operators via deeponet based on the universal approximation theorem of operators

Reference 21

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Observation 1a9a04b1-f9c0-4ca0-9dec-e14ae881be63 · outbound

This paper cites DeepXDE : A deep learning library for solving differential equations.

Interpretability and Generalization Bounds for Learning Spatial Physics DeepXDE : A deep learning library for solving differential equations

Reference 22

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Observation 45a1873f-adce-4ed4-afbb-f58066189da5 · outbound

This paper cites Progress measures for grokking via mechanistic interpretability.

Interpretability and Generalization Bounds for Learning Spatial Physics Progress measures for grokking via mechanistic interpretability

Reference 23

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Observation e1888790-bf90-4f6c-b83e-5332c8749ecc · outbound

This paper cites Fourierformer: Transformer meets generalized fourier integral theorem.

Interpretability and Generalization Bounds for Learning Spatial Physics Fourierformer: Transformer meets generalized fourier integral theorem

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T19:52:08.633667Z

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Observation 9e1a634c-e02a-4071-b9c9-eb37d704db31 · outbound

This paper cites Resnet after all: Neural odes and their numerical solution.

Interpretability and Generalization Bounds for Learning Spatial Physics Resnet after all: Neural odes and their numerical solution

Reference 25

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Observation 262e0baa-de7a-4402-bd3a-c6ac936e8caa · outbound

This paper cites Thermodynamically consistent physics-informed neural networks for hyperbolic systems.

Interpretability and Generalization Bounds for Learning Spatial Physics Thermodynamically consistent physics-informed neural networks for hyperbolic systems

Reference 26

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Source-reported events for the cited work

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Observation 821a9163-5aa2-4da8-bd7a-4bc694124f75 · outbound

This paper cites Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets.

Interpretability and Generalization Bounds for Learning Spatial Physics Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets

Reference 27

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Observation ec5e653b-629c-4f37-b016-a52da5db8e28 · outbound

This paper cites Continuous-in-Depth Neural Networks.

Interpretability and Generalization Bounds for Learning Spatial Physics Continuous-in-Depth Neural Networks

Reference 28

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Observation ef53f7e1-84d8-430e-8ac0-104e439a306f · outbound

This paper cites Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.

Interpretability and Generalization Bounds for Learning Spatial Physics Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Reference 29

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Observation 00402810-5cd1-4a0f-874b-38d34e1b8aec · outbound

This paper cites Data-driven discovery of partial differential equations.

Interpretability and Generalization Bounds for Learning Spatial Physics Data-driven discovery of partial differential equations

Reference 30

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Observation 18b5ee77-a921-4814-8160-f7d5b4633bef · outbound

This paper cites Do residual neural networks discretize neural ordinary differential equations? In S.

Interpretability and Generalization Bounds for Learning Spatial Physics Do residual neural networks discretize neural ordinary differential equations? In S

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:52:08.925419Z

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Observation 92d75ceb-ec13-4e00-abff-38a338cf7125 · outbound

This paper cites Exact solutions to the nonlinear dynamics of learning in deep linear neural networks.

Interpretability and Generalization Bounds for Learning Spatial Physics Exact solutions to the nonlinear dynamics of learning in deep linear neural networks

Reference 32

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Observation 62c424f6-7b0d-4fa9-99e8-fa1f4d8a6753 · outbound

This paper cites General-purpose foundation models for increased autonomy in robot-assisted surgery.

Interpretability and Generalization Bounds for Learning Spatial Physics General-purpose foundation models for increased autonomy in robot-assisted surgery

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:52:08.912004Z

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Observation 49c6a940-8b9e-42a2-932c-a5cb448488ed · outbound

This paper cites Enforcing exact physics in scientific machine learning: a data-driven exterior calculus on graphs.

Interpretability and Generalization Bounds for Learning Spatial Physics Enforcing exact physics in scientific machine learning: a data-driven exterior calculus on graphs

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:52:08.898943Z

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Observation 1e9e877e-af30-47bd-a63c-ad0aff43c8bf · outbound

This paper cites Ai feynman: A physics-inspired method for symbolic regression.

Interpretability and Generalization Bounds for Learning Spatial Physics Ai feynman: A physics-inspired method for symbolic regression

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-15T19:52:08.677327Z

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Observation 1642cbc6-0b0a-4885-8eea-343ae8efcb6b · outbound

This paper cites Nonlocal Attention Operator: Materializing Hidden Knowledge Towards Interpretable Physics Discovery.

Interpretability and Generalization Bounds for Learning Spatial Physics Nonlocal Attention Operator: Materializing Hidden Knowledge Towards Interpretable Physics Discovery

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T19:52:08.681047Z

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Observation db891539-1afe-4939-96f6-527fd8b4865b · outbound

This paper cites Trained transformers learn linear models in-context.

Interpretability and Generalization Bounds for Learning Spatial Physics Trained transformers learn linear models in-context

Reference 37

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Observation 249e3ad7-e83b-4eab-a865-66914e338863 · outbound

This paper cites On numerical integration in neural ordinary differential equations.

Interpretability and Generalization Bounds for Learning Spatial Physics On numerical integration in neural ordinary differential equations

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:52:08.868955Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

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