Paper Citation Record · LEDGER

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor

As of 8 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 17 inbound Pith citation observations for arXiv:2505.22904.

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

pith.paper-citation-record.v1
2505.22904 v2

Coverage vector

measured 62 of 62 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:04:16.498209Z

measured 79 of 79 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T14:00:11.843976Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

62 of 62 outbound references displayed

verified exact1
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External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 4f7fc3d1-abd5-4d37-9501-367477786a01 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor On the Opportunities and Risks of Foundation Models

Reference 1

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Observation 12313a34-3ff9-4613-84d9-7eb08e82f4c5 · outbound

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

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Learning nonlinear operators via deeponet based on the universal approximation theorem of operators

Reference 2

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Observation 88a790ac-c1c0-431e-b119-2c4ebda7d034 · outbound

This paper cites Ultra-fast meta-solvers for 3d scattering problems using neural operators trained as foundation models.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Ultra-fast meta-solvers for 3d scattering problems using neural operators trained as foundation models

Reference 3

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Observation 769099d0-9032-4c8f-8ee8-4d4ad7dbb67a · outbound

This paper cites Fourier Neural Operator for Parametric Partial Differential Equations.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Fourier Neural Operator for Parametric Partial Differential Equations

Reference 4

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Observation 0b951627-90c0-4a68-9b72-41ae34452874 · outbound

This paper cites FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators

Reference 5

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Observation c0c2cd4c-c5d8-4987-934b-3ce09e13df9b · outbound

This paper cites Towards foundation models for scientific machine learning: Characterizing scaling and transfer behavior.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Towards foundation models for scientific machine learning: Characterizing scaling and transfer behavior

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:21.269365Z

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Observation d14acd18-c3bd-4a5d-8d48-07de74cc3b01 · outbound

This paper cites Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries

Reference 7

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Observation a5399943-f3c5-4303-986f-7aee2cdcf599 · outbound

This paper cites A foundation model for atomistic materials chemistry.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor A foundation model for atomistic materials chemistry

Reference 8

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Observation ad73ea1d-d66c-4c01-8e5e-febb8f37b239 · outbound

This paper cites Mole: a foundation model for molecular graphs using disentangled attention.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Mole: a foundation model for molecular graphs using disentangled attention

Reference 9

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Observation 9938618d-f031-427d-9062-334afa537217 · outbound

This paper cites Developing a Foundation Model for Predicting Material Failure.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Developing a Foundation Model for Predicting Material Failure

Reference 10

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Observation 9ff4e5bd-1fe4-4fa8-a74b-88bfeb01d695 · outbound

This paper cites Foundation Models for Generalist Geospatial Artificial Intelligence.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 11

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Observation c8414783-d10f-437e-a87e-e8f4acae5b07 · outbound

This paper cites Attention is all you need.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Attention is all you need

Reference 12

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Observation 3ec98c12-4d5d-42e9-984a-f4c9fc809dd1 · outbound

This paper cites Language models are few-shot learners.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Language models are few-shot learners

Reference 13

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Observation 9d46d1fc-735d-4443-a56d-2b1f0d174589 · outbound

This paper cites A survey of gpt-3 family large language models including chatgpt and gpt-4.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor A survey of gpt-3 family large language models including chatgpt and gpt-4

Reference 14

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Observation ce50f106-9a26-421a-aab0-ae820f253887 · outbound

This paper cites Sparks of artificial general intelligence: Early experiments with gpt-4, 2023.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Sparks of artificial general intelligence: Early experiments with gpt-4, 2023

Reference 15

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Observation 1667d98f-3ed6-4d4c-9952-919e9a1b9cb9 · outbound

This paper cites Emerging properties in self-supervised vision transformers.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Emerging properties in self-supervised vision transformers

Reference 16

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Observation e94aa284-cbd7-44f2-a602-8178c2113ae9 · outbound

This paper cites Segment anything.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Segment anything

Reference 17

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Observation 29a0fcc5-2764-4832-92c6-2c83ea4153ec · outbound

This paper cites Learning transferable visual models from natural language supervision.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Learning transferable visual models from natural language supervision

Reference 18

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Observation 748b20ef-9d91-4c21-b3af-79257c59d544 · outbound

This paper cites Zero-shot text-to-image generation.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Zero-shot text-to-image generation

Reference 19

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Observation 5d4dab10-3787-4d5b-b785-5c9391d46d4a · outbound

This paper cites High- resolution image synthesis with latent diffusion models.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor High- resolution image synthesis with latent diffusion models

Reference 20

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This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Gemini: A Family of Highly Capable Multimodal Models

Reference 21

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Observation cb7ca296-a4e3-44a3-8215-0db8ba862da1 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 22

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Observation a4573f5a-1eee-458d-a81b-964932fc5319 · outbound

This paper cites DeepSeek-V3 Technical Report.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor DeepSeek-V3 Technical Report

Reference 23

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Observation b1304888-67a8-4dd7-a834-844044634104 · outbound

This paper cites Lora: Low-rank adaptation of large language models.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Lora: Low-rank adaptation of large language models

Reference 24

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Observation e7cacb84-4c43-4689-80cc-c79b95c84f9d · outbound

This paper cites The well: a large-scale collection of diverse physics simulations for machine learning.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor The well: a large-scale collection of diverse physics simulations for machine learning

Reference 25

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Observation 44ae1e7f-e1bc-49bf-abd8-a74617701eb7 · outbound

This paper cites A static condensation reduced basis element method: approximation and a posteriori error estimation.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor A static condensation reduced basis element method: approximation and a posteriori error estimation

Reference 26

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Observation 00bc6a1a-4902-449b-a2b8-d9db37c9ad54 · outbound

This paper cites A static condensation reduced basis element method: Complex problems.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor A static condensation reduced basis element method: Complex problems

Reference 27

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Observation 3e99720e-27fc-4031-bcc3-00de62bc9d46 · outbound

This paper cites A port-reduced static condensation reduced basis element method for large component-synthesized structures: approximation and a posteriori error estimation.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor A port-reduced static condensation reduced basis element method for large component-synthesized structures: approximation and a posteriori error estimation

Reference 28

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Observation 403914aa-d24e-4e2f-872c-ba7742db67ac · outbound

This paper cites Component-wise reduced order model lattice-type structure design.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Component-wise reduced order model lattice-type structure design

Reference 29

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Observation aa4fe6d6-4275-4083-bcd5-fe2bc729e561 · outbound

This paper cites Stress-constrained topology optimization of lattice-like structures using component-wise reduced order models.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Stress-constrained topology optimization of lattice-like structures using component-wise reduced order models

Reference 30

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Observation d5866542-6604-4cb8-bd09-1af46b184557 · outbound

This paper cites Train small, model big: Scalable physics simulators via reduced order modeling and domain decomposition.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Train small, model big: Scalable physics simulators via reduced order modeling and domain decomposition

Reference 31

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Observation 72c62809-aa6f-4997-b461-48af1c9e64e6 · outbound

This paper cites Scaled-up prediction of steady Navier-Stokes equation with component reduced order modeling.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Scaled-up prediction of steady Navier-Stokes equation with component reduced order modeling

Reference 32

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Observation 91d669cd-eecf-4438-903d-f03fed3aacb2 · outbound

This paper cites Scalable physics-guided data-driven component model reduction for steady Navier-Stokes flow.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Scalable physics-guided data-driven component model reduction for steady Navier-Stokes flow

Reference 33

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This paper cites Scalable nonlinear manifold reduced order model for dynamical systems.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Scalable nonlinear manifold reduced order model for dynamical systems

Reference 34

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This paper cites Domain-decomposition least-squares petrov– galerkin (dd-lspg) nonlinear model reduction.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Domain-decomposition least-squares petrov– galerkin (dd-lspg) nonlinear model reduction

Reference 35

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Observation 34d61698-eb88-4c83-8d44-5ec067acd962 · outbound

This paper cites A hyperreduced reduced basis element method for reduced-order modeling of component-based nonlinear systems.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor A hyperreduced reduced basis element method for reduced-order modeling of component-based nonlinear systems

Reference 36

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Observation e86c2ee9-6070-4213-b8cf-15ac6ebaf465 · outbound

This paper cites Optimal local approximation spaces for component- based static condensation procedures.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Optimal local approximation spaces for component- based static condensation procedures

Reference 37

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Observation 82afb4bd-302e-4e6f-94e0-a35a201a30c5 · outbound

This paper cites Port reduction in parametrized component static conden- sation: approximation and a posteriori error estimation.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Port reduction in parametrized component static conden- sation: approximation and a posteriori error estimation

Reference 38

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Observation 5154ea8a-451f-4a6a-bbfe-b65f33f1c940 · outbound

This paper cites A fast and accurate do- main decomposition nonlinear manifold reduced order model.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor A fast and accurate do- main decomposition nonlinear manifold reduced order model

Reference 39

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This paper cites The role of interface boundary conditions and sampling strategies for schwarz-based coupling of projection- based reduced order models.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor The role of interface boundary conditions and sampling strategies for schwarz-based coupling of projection- based reduced order models

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:19.411489Z

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Observation c528655c-c73d-4467-8e19-27608bd4ac5b · outbound

This paper cites A survey of projection-based model reduction methods for parametric dynamical systems.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor A survey of projection-based model reduction methods for parametric dynamical systems

Reference 41

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This paper cites Space–time least-squares petrov–galerkin projection for nonlinear model reduction.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Space–time least-squares petrov–galerkin projection for nonlinear model reduction

Reference 42

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This paper cites Conservative model reduction for finite-volume models.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Conservative model reduction for finite-volume models

Reference 43

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Observation fb9df992-832f-48b7-8930-fd950890214c · outbound

This paper cites Certified Reduced Basis Methods for Parametrized Partial Differential Equations.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Certified Reduced Basis Methods for Parametrized Partial Differential Equations

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:19.006347Z

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Observation 2b7daa97-631f-4514-abde-baf31ef299b7 · outbound

This paper cites A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder

Reference 45

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Observation d53ae8c7-d244-498d-b5b2-a17dd95c3c65 · outbound

This paper cites Model reduction of dynamical systems on nonlinear mani- folds using deep convolutional autoencoders.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Model reduction of dynamical systems on nonlinear mani- folds using deep convolutional autoencoders

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:18.835044Z

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Observation 40a4e14c-10c5-484f-962b-996d1c0ada32 · outbound

This paper cites Reduced Basis Methods for Partial Differential Equations: An Introduction, volume 92.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Reduced Basis Methods for Partial Differential Equations: An Introduction, volume 92

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:18.632524Z

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Observation 03f8fdab-a90e-4ca7-a057-0ac44a79414d · outbound

This paper cites Proper orthogonal decomposition closure models for turbulent flows: a numerical comparison.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Proper orthogonal decomposition closure models for turbulent flows: a numerical comparison

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:18.444999Z

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Observation cbf81e10-acb3-49ef-90c5-92d120a68160 · outbound

This paper cites Local Reduced-Order Modeling for Electrostatic Plasmas by Physics-Informed Solution Manifold Decomposition.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Local Reduced-Order Modeling for Electrostatic Plasmas by Physics-Informed Solution Manifold Decomposition

Reference 49

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Observation dbbac343-96e3-4c3c-af19-7c583997dc03 · outbound

This paper cites Simultaneous analysis and design in PDE-constrained optimization.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Simultaneous analysis and design in PDE-constrained optimization

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:18.302244Z

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Observation 9b7fcaa9-c550-42b2-a4ba-cbbbaf81cdbd · outbound

This paper cites Discovering governing equations from data by sparse identification of nonlinear dynamical systems.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Discovering governing equations from data by sparse identification of nonlinear dynamical systems

Reference 51

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Source-reported events for the cited work

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Observation a67fbac2-f6e8-418c-8468-43726b1039dc · outbound

This paper cites Weak sindy for partial differential equations.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Weak sindy for partial differential equations

Reference 52

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Observation 01e4d5ab-c693-45f2-b578-7e8b556a408d · outbound

This paper cites A finite element method for crack growth without remeshing.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor A finite element method for crack growth without remeshing

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:18.103786Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bd7ba77c-8b1d-4bee-aced-3e7b0beeef2c · outbound

This paper cites Generalized multiscale finite element methods (gmsfem).

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Generalized multiscale finite element methods (gmsfem)

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:17.970200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fb9115ac-6e31-456a-8957-350fd5d46eb8 · outbound

This paper cites Multiscale finite element methods for high- contrast problems using local spectral basis functions.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Multiscale finite element methods for high- contrast problems using local spectral basis functions

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:17.778040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2e2a30f1-3820-4dd9-a693-1942c4f1cecb · outbound

This paper cites Randomized oversampling for generalized multiscale finite element methods.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Randomized oversampling for generalized multiscale finite element methods

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:17.629588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a60fc950-917c-437c-be5f-f148b875fb5c · outbound

This paper cites Constraint energy minimizing generalized multiscale finite element method.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Constraint energy minimizing generalized multiscale finite element method

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:17.462064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 083dc5a0-9fe6-4b5e-807d-3182e7da6dd6 · outbound

This paper cites Generalized multiscale finite element method.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Generalized multiscale finite element method

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:17.343330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f8f695a4-c9ca-4e01-8141-5cec1c4df2cc · outbound

This paper cites Constraint energy minimizing generalized multiscale discontinuous galerkin method.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Constraint energy minimizing generalized multiscale discontinuous galerkin method

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:17.208629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b5c6dc2c-b1e6-46f8-a53f-2e4644ee6bd8 · outbound

This paper cites Im- plicit neural representations with periodic activation functions.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Im- plicit neural representations with periodic activation functions

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T13:04:16.344001Z

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Observation 03ab83e5-d6f4-46ed-ba01-f4f5dcaf8214 · outbound

This paper cites DeBERTa: Decoding-enhanced BERT with Disentangled Attention.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor DeBERTa: Decoding-enhanced BERT with Disentangled Attention

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T13:04:16.419765Z

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Observation 6c346396-2310-4e2b-ad00-448425fe9148 · outbound

This paper cites Smirk: An atomi- cally complete tokenizer for molecular foundation models.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Smirk: An atomi- cally complete tokenizer for molecular foundation models

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T13:04:16.498209Z

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Pith citing papers

Observation ae96ad7c-d16f-430c-89d4-5020dfd1a348 · inbound

Generative Latent Space Dynamics of Electron Density cites this paper.

Generative Latent Space Dynamics of Electron Density Defining Foundation Models for Computational Science: A Call for Clarity and Rigor

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-05T14:00:11.843976Z

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Observation 19c4676d-87bc-4e59-b9e7-87e66ca9e62b · inbound

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Model Order Reduction for Quantum Molecular Dynamics Defining Foundation Models for Computational Science: A Call for Clarity and Rigor

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T22:27:43.617172Z

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Observation 7c2e5102-b40e-4fdf-8a6e-e459a70af728 · inbound

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Hybrid coupling with operator inference and the overlapping Schwarz alternating method Defining Foundation Models for Computational Science: A Call for Clarity and Rigor

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T21:07:05.841744Z

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Observation 184a1889-4bdf-4d67-a45e-551c991f3852 · inbound

ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms cites this paper.

ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms Defining Foundation Models for Computational Science: A Call for Clarity and Rigor

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-17T01:58:51.661334Z

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Observation f618b8c2-42cf-4830-a35b-1cc4eba7388b · inbound

ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms cites this paper.

ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms Defining Foundation Models for Computational Science: A Call for Clarity and Rigor

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T18:51:30.869886Z

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Observation 32ba62c7-e6f3-49cc-80f6-71196f2230ce · inbound

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Structure-Preserving Learning Improves Geometry Generalization in Neural PDEs Defining Foundation Models for Computational Science: A Call for Clarity and Rigor

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-03T05:22:10.999416Z

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Observation f2e1b2c9-e5dc-4196-a7fc-f3065cd67135 · inbound

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SLE-FNO: Single-Layer Extensions for Task-Agnostic Continual Learning in Fourier Neural Operators Defining Foundation Models for Computational Science: A Call for Clarity and Rigor

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-15T08:09:51.343998Z

Source-reported events for the cited work

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Observation d887263e-a0d3-4df2-94fc-9f8253bacaee · inbound

Challenges and opportunities for AI to help deliver fusion energy cites this paper.

Challenges and opportunities for AI to help deliver fusion energy Defining Foundation Models for Computational Science: A Call for Clarity and Rigor

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T00:43:25.447811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 91466c6a-badc-470f-a9d6-788f8ee9af9f · inbound

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Reference 17

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T05:36:04.744326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 45c25c80-3e7a-4b95-bf30-85dcf5df0e0f · inbound

Component-Based Reduced-Order Modeling Framework for Rocket Combustion Dynamics in Multi-Injector Configurations cites this paper.

Component-Based Reduced-Order Modeling Framework for Rocket Combustion Dynamics in Multi-Injector Configurations Defining Foundation Models for Computational Science: A Call for Clarity and Rigor

Reference 61

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T09:28:38.365775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 02307b76-e62f-4e90-90fc-6cabbaf3b415 · inbound

A Hybridizable Neural Time Integrator for Stable Autoregressive Forecasting cites this paper.

A Hybridizable Neural Time Integrator for Stable Autoregressive Forecasting Defining Foundation Models for Computational Science: A Call for Clarity and Rigor

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-10T00:14:46.509041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0dee7b1d-c4eb-4dee-9862-1a15fbde1f31 · inbound

Replay-Based Continual Learning for Physics-Informed Neural Operators cites this paper.

Replay-Based Continual Learning for Physics-Informed Neural Operators Defining Foundation Models for Computational Science: A Call for Clarity and Rigor

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:41:05.477300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation dfa17a79-1eb5-4aff-ac76-25b095b6643d · inbound

From inverse problems to neural operators: prediction, mechanism, and generalization of data-driven models cites this paper.

From inverse problems to neural operators: prediction, mechanism, and generalization of data-driven models Defining Foundation Models for Computational Science: A Call for Clarity and Rigor

Reference 71

Resolution: verified exact; arxiv_id, observed 2026-07-03T00:47:30.234347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b72fdf33-aaa9-4945-a4e1-f3101089c983 · inbound

From inverse problems to neural operators: prediction, mechanism, and generalization of data-driven models cites this paper.

From inverse problems to neural operators: prediction, mechanism, and generalization of data-driven models Defining Foundation Models for Computational Science: A Call for Clarity and Rigor

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-02T12:05:40.715047Z

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Observation 3fb66b9d-6fad-44af-af32-fe65c526406d · inbound

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Landsat-Sentinel-2 Algal Bloom Mapping Using Vision Transformers: Model Description, Implementation, and Examples Defining Foundation Models for Computational Science: A Call for Clarity and Rigor

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T17:38:44.333750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bc85fb41-3a8d-4aa2-bb5b-958c0cc0d3bf · inbound

Advances in Scientific Machine Learning for Coupled Fluid Flow and Transport cites this paper.

Advances in Scientific Machine Learning for Coupled Fluid Flow and Transport Defining Foundation Models for Computational Science: A Call for Clarity and Rigor

Reference 20

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T00:49:19.500148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9ef4bfa2-03f0-4310-af55-aa8db5e9c882 · inbound

Robust and Explainable 3D Mode Shape Recognition Using Region-Aware Graph Neural Networks cites this paper.

Robust and Explainable 3D Mode Shape Recognition Using Region-Aware Graph Neural Networks Defining Foundation Models for Computational Science: A Call for Clarity and Rigor

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T19:08:49.318547Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.