Paper Citation Record · LEDGER

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning

As of 17 August 2026, this Paper Citation Record lists 100 of 210 outbound references and 9 inbound Pith citation observations for arXiv:2412.00568.

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

pith.paper-citation-record.v1
2412.00568 v2

Coverage vector

measured 100 of 210 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T05:16:55.519558Z

measured 109 of 109 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:58:11.415442Z

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

100 of 210 outbound references displayed

verified exact1
verified fuzzy0
unresolved99
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

6
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 5fc886ba-7d73-47f4-8e67-83ed6273502f · outbound

This paper cites Eyring, S.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Eyring, S

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.036204Z

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Observation 343ccf2e-4d28-49d7-ba3b-21a02b318ab5 · outbound

This paper cites Berger and Randall J.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Berger and Randall J

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.042582Z

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Observation 4cb62c10-b0f7-4800-8737-aedfa9d4a691 · outbound

This paper cites GraphCast: Learning skillful medium-range global weather forecasting.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning GraphCast: Learning skillful medium-range global weather forecasting

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.047621Z

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Observation 22836a0e-3d6b-48f7-b362-3fa01eb017e4 · outbound

This paper cites Large-scale pde-constrained optimization: an introduction.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Large-scale pde-constrained optimization: an introduction

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.052749Z

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Observation 99e744c2-e938-4658-a486-6456ee8ce6e4 · outbound

This paper cites Shape optimization in fluid mechanics.Annu.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Shape optimization in fluid mechanics.Annu

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.057619Z

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Observation ecd58b0d-faac-4363-ae2a-58cd9007edd0 · outbound

This paper cites The frontier of simulation-based inference.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning The frontier of simulation-based inference

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.062378Z

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Observation 8caa79d3-8e0f-4b4b-a022-e18443f27aee · outbound

This paper cites Simbig: Field-level simulation-based inference of galaxy clustering, 2023.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Simbig: Field-level simulation-based inference of galaxy clustering, 2023

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.067780Z

Source-reported events for the cited work

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Observation 7d415e2c-aba4-48ad-9c6f-181f3ab5e341 · outbound

This paper cites American Mathematical Society, 2022.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning American Mathematical Society, 2022

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.072769Z

Source-reported events for the cited work

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Observation a082bdb8-e638-4074-8c71-27f93169e791 · outbound

This paper cites Queipo, Raphael T.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Queipo, Raphael T

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.078136Z

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Observation 5196872a-b5fe-4a39-9fee-5ccbd9baf991 · outbound

This paper cites Forrester, A.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Forrester, A

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.083061Z

Source-reported events for the cited work

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Observation d511ef98-b0d8-47ee-b2c9-ae6a6d57918c · outbound

This paper cites Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data.Computer Methods in Applied Mechanics and Engineering, 361:112732, 2020.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data.Computer Methods in Applied Mechanics and Engineering, 361:112732, 2020

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.087520Z

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Observation 7fae4c82-f415-42fe-a990-54afa224d619 · outbound

This paper cites Application of deep learning based multi-fidelity surrogate model to robust aerodynamic design optimization.Aerospace Science and T echnology, 92:722–737, 2019.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Application of deep learning based multi-fidelity surrogate model to robust aerodynamic design optimization.Aerospace Science and T echnology, 92:722–737, 2019

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.091804Z

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Observation 6d66dc4a-e0e3-47e6-bb0b-d58daa727b03 · outbound

This paper cites A physics- informed deep learning framework for inversion and surrogate modeling in solid mechanics.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning A physics- informed deep learning framework for inversion and surrogate modeling in solid mechanics

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.096506Z

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Observation e82655ca-1253-44d6-891e-0c990b620f38 · outbound

This paper cites Neural-network quantum state tomography.Nature Physics, 14(5):447–450, 2018.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Neural-network quantum state tomography.Nature Physics, 14(5):447–450, 2018

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.101538Z

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Observation 74528bdf-849d-476d-8e61-2fabaec41988 · outbound

This paper cites Deep learning and density-functional theory.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Deep learning and density-functional theory

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.106908Z

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Observation c11a72a5-305d-47b4-b927-c29d049d251f · outbound

This paper cites Recent advances and applications of deep learning methods in materials science.npj Computational Materials, 8(1):59, 2022.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Recent advances and applications of deep learning methods in materials science.npj Computational Materials, 8(1):59, 2022

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.111055Z

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Observation 8dad7b35-0add-4eb7-a65b-f4e24a188106 · outbound

This paper cites Martian time-series unraveled: A multi-scale nested approach with factorial variational autoencoders.arXiv preprint arXiv:2305.16189, 2023.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Martian time-series unraveled: A multi-scale nested approach with factorial variational autoencoders.arXiv preprint arXiv:2305.16189, 2023

Reference 17

Resolution: verified exact; raw_fallback, observed 2026-08-12T05:16:56.474042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e90a2660-fa22-47ad-a51e-00764e042c33 · outbound

This paper cites Plasma surrogate modelling using fourier neural operators, 2023.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Plasma surrogate modelling using fourier neural operators, 2023

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.120383Z

Source-reported events for the cited work

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Observation b6e1213f-18c5-4531-b634-4d99e566ef1d · outbound

This paper cites Mahoney, and Amir Gholami.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Mahoney, and Amir Gholami

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.124794Z

Source-reported events for the cited work

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Observation 6f91df0a-4904-483e-b927-965fb2c42e6b · outbound

This paper cites Convolutional neural operators for robust and accurate learning of pdes, 2023.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Convolutional neural operators for robust and accurate learning of pdes, 2023

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.129256Z

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Observation 60a8492e-132c-413f-85f3-b6f00b38f500 · outbound

This paper cites Goldberg, Y an-Fei Jiang, and Lars Bildsten.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Goldberg, Y an-Fei Jiang, and Lars Bildsten

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.133544Z

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Observation 0048ccbc-02fa-4abb-8d2b-3b2784a9a948 · outbound

This paper cites The sequence read archive: explosive growth of sequencing data.Nucleic acids research, 40(D1):D54–D56, 2012.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning The sequence read archive: explosive growth of sequencing data.Nucleic acids research, 40(D1):D54–D56, 2012

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.138036Z

Source-reported events for the cited work

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Observation 597853c0-fb0e-48aa-816d-a10109e1f63e · outbound

This paper cites The data deluge: An e-science perspective.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning The data deluge: An e-science perspective

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.142461Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8d475a6-3347-4de3-892a-642bf27c3331 · outbound

This paper cites Training deep surrogate models with large scale online learning.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Training deep surrogate models with large scale online learning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.147117Z

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Observation 925de82a-5b19-4776-b5c7-b9d924175ef0 · outbound

This paper cites GPT-4 Technical Report.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning GPT-4 Technical Report

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.151503Z

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Observation efa51699-0f0c-4e4d-b4e6-f041f4e680c7 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.156570Z

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Observation 3c80fafb-4094-40d6-a739-6f9c4e77423e · outbound

This paper cites The Falcon Series of Open Language Models.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning The Falcon Series of Open Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.162198Z

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Unavailable: canonical work link unavailable.

Observation 52c4f0de-0f15-4df4-ba29-55685b0d22e8 · outbound

This paper cites Scaling Rectified Flow Transformers for High-Resolution Image Synthesis.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Scaling Rectified Flow Transformers for High-Resolution Image Synthesis

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.167837Z

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Observation b6755e0f-294b-454c-8cb9-c76cb867acb5 · outbound

This paper cites Asynchronous pipeline for processing huge corpora on medium to low resource infrastructures.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Asynchronous pipeline for processing huge corpora on medium to low resource infrastructures

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.172945Z

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Observation 1bd7b724-69b8-4464-8769-14609c6660af · outbound

This paper cites Obelics: An open web-scale filtered dataset of interleaved image-text documents.Advances in Neural Information Processing Systems, 36, 2024.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Obelics: An open web-scale filtered dataset of interleaved image-text documents.Advances in Neural Information Processing Systems, 36, 2024

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.177566Z

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Observation abad394d-dca9-4e8f-be8a-a629ab2547cc · outbound

This paper cites The Pile: An 800GB Dataset of Diverse Text for Language Modeling.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.182735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec4b64ba-5942-400d-a435-10b0368bf0fc · outbound

This paper cites The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.188167Z

Source-reported events for the cited work

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Observation 22bf7e28-e13b-4a0c-ad40-949704bf29c8 · outbound

This paper cites Laion-5b: An open large-scale dataset for training next generation image-text models.Advances in Neural Information Processing Systems, 35:25278–25294, 2022.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Laion-5b: An open large-scale dataset for training next generation image-text models.Advances in Neural Information Processing Systems, 35:25278–25294, 2022

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.193935Z

Source-reported events for the cited work

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Observation 08dee554-ad9d-4971-851d-173bfa852d7b · outbound

This paper cites Scaling Language Models: Methods, Analysis & Insights from Training Gopher.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.199199Z

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Observation daf94c6e-97d9-45c1-abb4-c7a030ad9b89 · outbound

This paper cites Textbooks Are All You Need II: phi-1.5 technical report.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Textbooks Are All You Need II: phi-1.5 technical report

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.204059Z

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Observation 3921c8dd-65e2-4bec-91ba-5e2fad5adedf · outbound

This paper cites Pdebench: An extensive benchmark for scientific machine learning.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Pdebench: An extensive benchmark for scientific machine learning

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.209207Z

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Observation c259e428-3908-41d4-aa90-522debbe43a8 · outbound

This paper cites Towards Multi-spatiotemporal-scale Generalized PDE Modeling.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Towards Multi-spatiotemporal-scale Generalized PDE Modeling

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.214692Z

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Observation 578997db-9a6a-4dd0-92d4-fcb84cd47d8c · outbound

This paper cites PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.219509Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0db8c5b6-ee3a-4737-a0dc-b3edcb5d5b14 · outbound

This paper cites Benchmarking autoregressive conditional diffusion models for turbulent flow simulation.arXiv, 2023.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Benchmarking autoregressive conditional diffusion models for turbulent flow simulation.arXiv, 2023

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.224368Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7764a6f1-7bc8-4bad-a169-c0548498f60f · outbound

This paper cites Airfrans: High fidelity computational fluid dynamics dataset for approximating reynolds-averaged navier–stokes solutions.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Airfrans: High fidelity computational fluid dynamics dataset for approximating reynolds-averaged navier–stokes solutions

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.229078Z

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Observation 4265906b-b0ae-4282-b9fb-71f10f58ff7d · outbound

This paper cites Lagrangebench: A lagrangian fluid mechanics benchmarking suite.Advances in Neural Information Processing Systems, 36, 2024.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Lagrangebench: A lagrangian fluid mechanics benchmarking suite.Advances in Neural Information Processing Systems, 36, 2024

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.234072Z

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Observation 64d34bb9-57d2-44f2-aaa3-b4b3e34dd216 · outbound

This paper cites The era5 global reanalysis.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning The era5 global reanalysis

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.239438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00681055-f7f5-4cf7-aa04-6d98d074f726 · outbound

This paper cites David Neelin, David Randall, Sara Shamekh, Mark A T aylor, Nathan Urban, Janni Y uval, Guang Zhang, and Michael Pritchard.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning David Neelin, David Randall, Sara Shamekh, Mark A T aylor, Nathan Urban, Janni Y uval, Guang Zhang, and Michael Pritchard

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.244363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f8929e8-fe4e-4b82-bdc4-248164970fda · outbound

This paper cites Eagle: Large-scale learning of turbulent fluid dynamics with mesh transformers.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Eagle: Large-scale learning of turbulent fluid dynamics with mesh transformers

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.249144Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d304d4d-cfd8-4058-8fb8-89d64235b136 · outbound

This paper cites BubbleML: A multi-physics dataset and benchmarks for machine learning.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning BubbleML: A multi-physics dataset and benchmarks for machine learning

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.254075Z

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Observation 60088257-c01e-4ba8-a35d-6876c86a51b5 · outbound

This paper cites Lips-learning industrial physical simulation benchmark suite.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Lips-learning industrial physical simulation benchmark suite

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.258723Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0fb486f1-8cb1-461f-b5be-cdadde5a4ee7 · outbound

This paper cites Chen, Jack Guo, Davy Brouzet, Mohsen T alei, Bruno Savard, Alexei Y.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Chen, Jack Guo, Davy Brouzet, Mohsen T alei, Bruno Savard, Alexei Y

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.263298Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8ed3ba0-bc87-40b3-8a98-bbe11856e6cb · outbound

This paper cites A public turbulence database cluster and applications to study lagrangian evolution of velocity increments in turbulence.Journal of Turbulence, (9):N31, 2008.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning A public turbulence database cluster and applications to study lagrangian evolution of velocity increments in turbulence.Journal of Turbulence, (9):N31, 2008

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.267989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c30b75a-648c-4fa6-b330-88fb127bea3f · outbound

This paper cites Multiple Physics Pretraining for Physical Surrogate Models.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Multiple Physics Pretraining for Physical Surrogate Models

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.272444Z

Source-reported events for the cited work

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Observation f32e51e3-ea2a-4641-81f1-d8f1bc1f4170 · outbound

This paper cites an unresolved cited work.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Unresolved cited work

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.277717Z

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Observation 484f0a86-74ac-4fd6-b900-79fdb141c9b3 · outbound

This paper cites Pretraining Codomain Attention Neural Operators for Solving Multiphysics PDEs.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Pretraining Codomain Attention Neural Operators for Solving Multiphysics PDEs

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.283016Z

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Observation ae1709e3-c6ba-492f-9618-cd62a29d9913 · outbound

This paper cites Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolation.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolation

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.288067Z

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Observation 16d597e1-f21a-409d-8e7e-df366ee13c6d · outbound

This paper cites UPS: Efficiently Building Foundation Models for PDE Solving via Cross-Modal Adaptation.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning UPS: Efficiently Building Foundation Models for PDE Solving via Cross-Modal Adaptation

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.293519Z

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Observation a6019dd5-9d1c-45ea-9745-b128a4578403 · outbound

This paper cites Poseidon: Efficient foundation models for pdes, 2024.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Poseidon: Efficient foundation models for pdes, 2024

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.299000Z

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Observation 4417511c-a804-40fa-ae1f-c168ff40b29f · outbound

This paper cites h5py/h5py: 3.8.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning h5py/h5py: 3.8

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.303466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a88da0e-f055-48e1-832b-46fb9039ca11 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library, 2019.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Pytorch: An imperative style, high-performance deep learning library, 2019

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.308083Z

Source-reported events for the cited work

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Observation 5d4aaeec-014f-43b1-9598-c93b77520204 · outbound

This paper cites Array programming with numpy.Nature, 585(7825):357–362, 2020.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Array programming with numpy.Nature, 585(7825):357–362, 2020

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.312490Z

Source-reported events for the cited work

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Observation 31b1a080-cfb9-4daf-930d-bc305fa7e7a2 · outbound

This paper cites Goldberg, Y an-Fei Jiang, and Lars Bildsten.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Goldberg, Y an-Fei Jiang, and Lars Bildsten

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.318525Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b98807c-fa77-4ea1-961f-7dd13d6c6007 · outbound

This paper cites Böhm-Vitense.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Böhm-Vitense

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.323124Z

Source-reported events for the cited work

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Observation 84c838af-c195-438d-8730-1bc388742307 · outbound

This paper cites an unresolved cited work.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Unresolved cited work

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.327548Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f16f6337-f70f-4048-9a96-d834791c5536 · outbound

This paper cites A Review of the Mixing Length Theory of Convection in 1D Stellar Modeling.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning A Review of the Mixing Length Theory of Convection in 1D Stellar Modeling

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.332227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2520c30b-ff33-4969-9954-3650f8c49802 · outbound

This paper cites Chiavassa, R.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Chiavassa, R

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.336833Z

Source-reported events for the cited work

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Observation 910b641c-036f-4b2c-b305-0fe0efe5505e · outbound

This paper cites Chiavassa, B.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Chiavassa, B

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.341712Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3da3e026-e242-4979-b54e-01fa12654645 · outbound

This paper cites Chiavassa, K.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Chiavassa, K

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.346379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 545b1cc3-887a-41e8-8e4b-89053f017a66 · outbound

This paper cites Goldberg.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Goldberg

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.350990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6df9b5e0-4e36-4380-9b81-4d18630aac9c · outbound

This paper cites Lax and Xu-Dong Liu.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Lax and Xu-Dong Liu

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.355983Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aaac5980-b721-40ac-869f-af1aa44c8008 · outbound

This paper cites an unresolved cited work.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Unresolved cited work

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.360992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9d89cca-f76d-4101-9c05-f8a7982b5e21 · outbound

This paper cites Gray and S.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Gray and S

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.366341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8fb31000-5f6b-4ee3-ac80-b84a31e3998b · outbound

This paper cites Williamson, John B.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Williamson, John B

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.371879Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81b08dd9-c5e9-4fa3-8564-5b44af937505 · outbound

This paper cites Lattimer and D.N.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Lattimer and D.N

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.376611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dcde3334-5e9c-4ffe-879d-2cf3c625dd3c · outbound

This paper cites an unresolved cited work.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Unresolved cited work

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.381473Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 626f2c46-66c9-4e8e-adf1-7ba88dac6d40 · outbound

This paper cites Transient Events from Neutron Star Mergers.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Transient Events from Neutron Star Mergers

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.386977Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79af6886-bf6d-49e8-a850-c3a75316ba4f · outbound

This paper cites an unresolved cited work.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Unresolved cited work

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.392578Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9db9a328-902e-4329-811d-5949a0136844 · outbound

This paper cites an unresolved cited work.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Unresolved cited work

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.398257Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6f711c4-e0f2-4c57-9fd3-0d32c5032b1e · outbound

This paper cites an unresolved cited work.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Unresolved cited work

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.403930Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d40d4f03-ce53-47ab-8a52-f37f33023ea9 · outbound

This paper cites an unresolved cited work.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Unresolved cited work

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.408386Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54c988a6-2587-4ffb-9fd7-b77cd185b9a1 · outbound

This paper cites an unresolved cited work.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Unresolved cited work

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.412816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41457e06-b31d-43b5-83c1-3e4fccd691c9 · outbound

This paper cites an unresolved cited work.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Unresolved cited work

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.417860Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5037fd8e-2265-40c9-925d-ea4ce2d011f0 · outbound

This paper cites Steady rayleigh–bénard convection between no-slip boundaries.Journal of Fluid Mechanics, 933:R4, 2022.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Steady rayleigh–bénard convection between no-slip boundaries.Journal of Fluid Mechanics, 933:R4, 2022

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.422375Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2de48d0-fd49-438f-857f-0a072c007aad · outbound

This paper cites Cambridge University Press, 2001.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Cambridge University Press, 2001

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.426838Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 136c3df3-dd1f-42cd-88ca-414bacc79d5e · outbound

This paper cites Elsevier, 2001.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Elsevier, 2001

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.431329Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52b1ff2d-abd3-45e5-bd57-a24056c87322 · outbound

This paper cites An introduction to dynamic meteorology, volume 88.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning An introduction to dynamic meteorology, volume 88

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.436220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea34b5b4-dcdb-47cc-950b-639a7525b903 · outbound

This paper cites Cooling of electronic systems, volume 258.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Cooling of electronic systems, volume 258

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.440819Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b88546dd-0e8a-4bb8-abcb-66f09c5604e5 · outbound

This paper cites Transport phenomena in materials processing.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Transport phenomena in materials processing

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.446141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f195441c-b825-4f2f-bf73-fa99694ceea1 · outbound

This paper cites Stellar interiors: physical principles, structure, and evolution.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Stellar interiors: physical principles, structure, and evolution

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.450660Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e85cbf03-78e0-43b6-ad6f-f06cb6c3ab82 · outbound

This paper cites The instability of liquid surfaces when accelerated in a direction perpendicular to their planes.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning The instability of liquid surfaces when accelerated in a direction perpendicular to their planes

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.455919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b051bf0-30aa-4afe-be98-30cecbe706cf · outbound

This paper cites Academic press, 2015.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Academic press, 2015

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.460423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8e053a9-f494-45e1-bf6f-7b1a131215b5 · outbound

This paper cites Space-time energy spectra in turbulent shear flows.Physical Review Fluids, 6(10):100504, 2021.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Space-time energy spectra in turbulent shear flows.Physical Review Fluids, 6(10):100504, 2021

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.464760Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2814a689-f3fb-4364-99c9-e89b0162e6f3 · outbound

This paper cites Self-organization of autophoretic suspensions in confined shear flows.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Self-organization of autophoretic suspensions in confined shear flows

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.469560Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37816643-e910-47f6-bb40-b8945b2f42d2 · outbound

This paper cites Separated and vortical flow in aircraft aerodynamics: a cfd perspective.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Separated and vortical flow in aircraft aerodynamics: a cfd perspective

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.474360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0efc294a-83f4-4b20-a683-2dd3724827c4 · outbound

This paper cites Ocean mixing by kelvin-helmholtz instability.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Ocean mixing by kelvin-helmholtz instability

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.478983Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e162532a-6b8e-4261-bc2e-450c1c66f5e9 · outbound

This paper cites Assessment of turbulent blood flow and wall shear stress in aortic coarctation using image-based simulations.Biomedical engineering online, 20(1):84, 2021.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Assessment of turbulent blood flow and wall shear stress in aortic coarctation using image-based simulations.Biomedical engineering online, 20(1):84, 2021

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.483383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 101ecebd-c853-4539-a2fd-c592c180825e · outbound

This paper cites an unresolved cited work.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Unresolved cited work

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.487739Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 209980b0-29e9-4cb0-aed6-8e62722a8190 · outbound

This paper cites Abruzzo, Drummond B.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Abruzzo, Drummond B

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.492299Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c05461de-3f85-48ba-997a-92e6f4914862 · outbound

This paper cites Fielding, Eve C.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Fielding, Eve C

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.497037Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4bb31137-fd13-4bcf-8437-b67ecd624a59 · outbound

This paper cites Multistability of elasto-inertial two-dimensional channel flow.Journal of Fluid Mechanics, 981:A30, 2024.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Multistability of elasto-inertial two-dimensional channel flow.Journal of Fluid Mechanics, 981:A30, 2024

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.501356Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e033792a-e577-4452-9a21-87b52e11e2d8 · outbound

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

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Fourier Neural Operator for Parametric Partial Differential Equations

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.505751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2a34beb-0b21-4e42-865b-8a2c1d6f13d6 · outbound

This paper cites Multi-grid tensorized fourier neural operator for high resolution PDEs, 2023.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning Multi-grid tensorized fourier neural operator for high resolution PDEs, 2023

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.510818Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 979ca97d-85e5-44b9-81e2-1f365bfb85bd · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning U-net: Convolutional networks for biomedical image segmentation

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.515253Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6cd60669-2e85-4936-83f5-5e808526af29 · outbound

This paper cites A convnet for the 2020s, 2022.

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning A convnet for the 2020s, 2022

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:55.519558Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation aee3700a-4a4e-4e87-8dff-88a418a9cb65 · inbound

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates cites this paper.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-11T13:31:12.205793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 545d9422-f0be-481f-afea-ab5792d0bbd0 · inbound

Neural Network Modeling of Microstructure Complexity Using Digital Libraries cites this paper.

Neural Network Modeling of Microstructure Complexity Using Digital Libraries The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-10T00:25:04.070414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63686879-4bc8-40b1-a7ab-f998aa2822c7 · inbound

Predicting the Dynamics of Complex System via Multiscale Diffusion Autoencoder cites this paper.

Predicting the Dynamics of Complex System via Multiscale Diffusion Autoencoder The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-16T00:58:11.415442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0922f27-020b-4ed7-a321-983cf51d091b · inbound

Towards a Physics Foundation Model cites this paper.

Towards a Physics Foundation Model The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-04T16:32:59.300987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf194db3-e7bb-4de6-8390-de23267c6976 · inbound

Flow marching for a generative PDE foundation model cites this paper.

Flow marching for a generative PDE foundation model The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:51:25.572303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3a54786c-c502-435b-81e8-162641397ed6 · inbound

Semigroup Consistency as a Diagnostic for Learned Physics Simulators cites this paper.

Semigroup Consistency as a Diagnostic for Learned Physics Simulators The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-06-29T22:24:00.350589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5dc0ac85-a423-4d47-b0f3-8bdccbc873a2 · inbound

Sparse probes and murky physics: a case study of interpretability challenges in a foundation model for continuum dynamics cites this paper.

Sparse probes and murky physics: a case study of interpretability challenges in a foundation model for continuum dynamics The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-06-27T11:00:50.756256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 54cc8dc8-1816-4d46-a85b-6e0075e4bf53 · inbound

ThousandWorlds: A benchmark for climate emulation of potentially habitable exoplanets cites this paper.

ThousandWorlds: A benchmark for climate emulation of potentially habitable exoplanets The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T20:08:56.556675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 47a73769-9151-4233-919f-c0c3b17ff71e · inbound

Otter Weather: Skillful and Computationally Efficient Medium-Range Weather Forecasting cites this paper.

Otter Weather: Skillful and Computationally Efficient Medium-Range Weather Forecasting The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-07-04T15:49:57.711478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.