Paper Citation Record · LEDGER

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries

As of 20 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 1 inbound Pith citation observation for arXiv:2501.01453.

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

pith.paper-citation-record.v1
2501.01453 v2

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:01:24.485534Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:04:12.300621Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T13:04:16.954819Z

Reference resolution

51 of 51 outbound references displayed

verified exact1
verified fuzzy39
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 089a3803-07cd-45b8-8198-e6bd661d7615 · outbound

This paper cites Computational fluid dynamics of whole-body aircraft.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Computational fluid dynamics of whole-body aircraft

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:25.556955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c786e62f-edf2-483d-91a1-8e1ab6f3259c · outbound

This paper cites Immersed boundary methods.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Immersed boundary methods

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:25.539379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c0516b3f-52e5-41c9-86e8-5ef6d70737e9 · outbound

This paper cites Review of research on vehicles aerodynamic drag reduction methods.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Review of research on vehicles aerodynamic drag reduction methods

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:25.523385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 28e8f254-3037-4262-856b-a6e7ef571493 · outbound

This paper cites A computational approach to modeling cellular-scale blood flow in complex geometry.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries A computational approach to modeling cellular-scale blood flow in complex geometry

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:25.506931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c5d49a6d-9d9d-4cd2-bee6-a05cac6d6cf2 · outbound

This paper cites Patient-specific modeling of geometry and blood flow in large arteries.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Patient-specific modeling of geometry and blood flow in large arteries

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:25.490274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a3f8623f-2ab2-44de-8ad6-9c4e2364aff0 · outbound

This paper cites Other geometries in architecture: bubbles, knots and minimal surfaces.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Other geometries in architecture: bubbles, knots and minimal surfaces

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:25.472717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d4a7b249-6fdd-499d-bba7-cc63990cd452 · outbound

This paper cites Deep learning for reduced order modelling and efficient temporal evolution of fluid simulations.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Deep learning for reduced order modelling and efficient temporal evolution of fluid simulations

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:25.456811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bd6e49df-009e-4dcf-ba25-fd890606ab4f · outbound

This paper cites Recent advances and applications of deep learning methods in materials science.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Recent advances and applications of deep learning methods in materials science

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:25.440791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 77fec346-691f-45de-bbf9-0c43c9ac8645 · outbound

This paper cites Robust data-driven turbulence modeling for rans closures using a sciml approach for validation.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Robust data-driven turbulence modeling for rans closures using a sciml approach for validation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:25.424801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9d4a6c5d-23a7-47ba-99b9-70769af5410b · outbound

This paper cites Mycrunchgpt: A llm assisted framework for scientific machine learning.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Mycrunchgpt: A llm assisted framework for scientific machine learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:25.410082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3b8ea28f-dca8-4711-9fda-dd6eba795dc8 · outbound

This paper cites The emergence and impact of scientific machine learning in geophysical exploration.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries The emergence and impact of scientific machine learning in geophysical exploration

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:25.394781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b2ac9167-1a8d-41b1-a789-f100e85f7695 · outbound

This paper cites Scientific Machine Learning for Modeling and Discovery of Physical Systems with Quantified Uncertainty.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Scientific Machine Learning for Modeling and Discovery of Physical Systems with Quantified Uncertainty

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:25.379947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b9d258a2-4148-4982-911e-447a7d8cdb51 · outbound

This paper cites Integrating scientific machine learning and physics-based models for quantification of uncertainty in thermal properties of silica aerogel.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Integrating scientific machine learning and physics-based models for quantification of uncertainty in thermal properties of silica aerogel

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:25.365097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9f47ecd5-a3df-47ce-8976-3201c0e01736 · outbound

This paper cites Uncertainty quantifica- tion in scientific machine learning: Methods, metrics, and comparisons.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Uncertainty quantifica- tion in scientific machine learning: Methods, metrics, and comparisons

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:25.349609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6c7cf86d-71b3-4a84-9895-4d3907eab336 · outbound

This paper cites an unresolved cited work.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-08-10T23:01:25.334026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ea436208-b28c-41c2-86a1-d425f974c287 · outbound

This paper cites CFDBench: A large-scale benchmark for machine learning methods in fluid dynamics, 2024.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries CFDBench: A large-scale benchmark for machine learning methods in fluid dynamics, 2024

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:25.319184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 699fb30a-4dfe-4be2-948b-007b0cee2de5 · outbound

This paper cites MegaFlow2D: A parametric dataset for machine learning super-resolution in computational fluid dynamics simulations.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries MegaFlow2D: A parametric dataset for machine learning super-resolution in computational fluid dynamics simulations

Reference 17

Resolution: metadata mismatch; raw_fallback, observed 2026-08-10T23:01:24.878769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f02d0733-66cd-43a1-94e5-633d221fccf2 · outbound

This paper cites Rapid prediction of two-dimensional airflow in an operating room using scientific machine learning.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Rapid prediction of two-dimensional airflow in an operating room using scientific machine learning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:25.304014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4c8d1640-cc0b-4d59-8bb4-908e2203fc2f · outbound

This paper cites FlowBench: A Large Scale Benchmark for Flow Simulation over Complex Geometries.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries FlowBench: A Large Scale Benchmark for Flow Simulation over Complex Geometries

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T23:01:24.318295Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04ac990d-9c36-4d68-8cd8-b3481e99986e · outbound

This paper cites Effective geometric algorithms for im- mersed boundary method using signed distance field.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Effective geometric algorithms for im- mersed boundary method using signed distance field

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:25.272724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 57dffb1f-ad3a-413c-83b9-0ef9fb6e1805 · outbound

This paper cites Signed distance field enhanced fully resolved cfd-dem for simulation of granular flows involving multiphase fluids and irregularly shaped particles.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Signed distance field enhanced fully resolved cfd-dem for simulation of granular flows involving multiphase fluids and irregularly shaped particles

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:25.257393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5f931963-32bc-4e8e-9cf8-6ba2940bd103 · outbound

This paper cites Unit operation and process modeling with physics-informed machine learning.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Unit operation and process modeling with physics-informed machine learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:25.241798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 48b81069-432a-48bf-898f-641248c405a4 · outbound

This paper cites Data-driven modeling of hypersonic reentry flow with heat and mass transfer.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Data-driven modeling of hypersonic reentry flow with heat and mass transfer

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:25.224678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d3e9ed35-cfc2-468a-8436-db8fcd8a7d4d · outbound

This paper cites Computational challenges of viscous incompressible flows.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Computational challenges of viscous incompressible flows

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:25.206436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 470db470-76f8-4eec-ad9e-cff9ade728ae · outbound

This paper cites Review of machine learning for hydrodynamics, transport, and reactions in multiphase flows and reactors.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Review of machine learning for hydrodynamics, transport, and reactions in multiphase flows and reactors

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:25.189182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f07a8f5e-89dd-4c18-acf3-08e1c04a16f5 · outbound

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

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Towards foundation models for scientific machine learning: Characterizing scaling and transfer behavior

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:25.173215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ee9213da-42d0-49e5-99e3-0b5c067eaa8f · outbound

This paper cites Interpretable models for extrapolation in scientific machine learning.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Interpretable models for extrapolation in scientific machine learning

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:25.157323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b5c4de67-2a06-4bde-8b5d-bc74c172fbe6 · outbound

This paper cites Learning stiff chemical kinetics using extended deep neural operators.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Learning stiff chemical kinetics using extended deep neural operators

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:25.140574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3ed64e0c-c3c9-429a-9ea2-94b7ed9447e7 · outbound

This paper cites Normalizing flows for probabilistic modeling and inference.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Normalizing flows for probabilistic modeling and inference

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T23:01:24.375462Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be6be0a4-aaca-4374-8c5c-969fdd40e0c7 · outbound

This paper cites A transdisciplinary review of deep learning research and its relevance for water resources scientists.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries A transdisciplinary review of deep learning research and its relevance for water resources scientists

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:25.114228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5e55cbe9-bee2-4489-b066-07bc827c6d43 · outbound

This paper cites Modeling and simulations of high-density two-phase flows using projection-based Cahn-Hilliard Navier-Stokes equations.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Modeling and simulations of high-density two-phase flows using projection-based Cahn-Hilliard Navier-Stokes equations

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-10T23:01:24.385718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75e8f721-3a8c-4027-8ca7-75f7e67baca2 · outbound

This paper cites Neural pde solvers for irregular domains.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Neural pde solvers for irregular domains

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:25.097350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b21efde0-fc86-4a3c-a757-5a8ac44092de · outbound

This paper cites Neufenet: Neural finite element solutions with theoretical bounds for parametric pdes.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Neufenet: Neural finite element solutions with theoretical bounds for parametric pdes

Reference 34

Resolution: verified exact; doi, observed 2026-08-10T23:01:24.538148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6370d514-4115-49d4-90d5-a7a500e24830 · outbound

This paper cites The shifted boundary method for embedded domain computations.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries The shifted boundary method for embedded domain computations

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:25.081478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bab40019-4343-4e83-a48c-1d452ef59beb · outbound

This paper cites Optimal surrogate boundary selection and scalability studies for the shifted boundary method on octree meshes.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Optimal surrogate boundary selection and scalability studies for the shifted boundary method on octree meshes

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:25.288723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f5479d69-172f-4446-bfe4-e1340dd15095 · outbound

This paper cites The NURBS book.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries The NURBS book

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-10T23:01:24.413298Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74cc250e-8545-4ed0-b4be-d8c94e8ae272 · outbound

This paper cites A simple method for particle shape generation with spher- ical harmonics.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries A simple method for particle shape generation with spher- ical harmonics

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:25.053769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6d59599e-2aec-4087-8bbf-5ea89e050eb6 · outbound

This paper cites Skelneton 2019: Dataset and challenge on deep learning for geometric shape understanding.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Skelneton 2019: Dataset and challenge on deep learning for geometric shape understanding

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:25.036704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2d90c9b9-59d2-4c22-b47f-ca1a95141336 · outbound

This paper cites Pyramid u-network for skeleton extraction from shape points.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Pyramid u-network for skeleton extraction from shape points

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:25.018400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 65e146bf-d129-4890-9449-c1dc762d0114 · outbound

This paper cites Internal flows.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Internal flows

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:25.003176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a4c0a939-84b9-4e64-aa58-78ab2fe1af5e · outbound

This paper cites Laminar, transitional, and turbulent flows in rotor-stator cavi- ties.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Laminar, transitional, and turbulent flows in rotor-stator cavi- ties

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:24.987718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8b562b82-78f0-49ad-a5d5-cafa25724e30 · outbound

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

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Poseidon: Efficient foundation models for pdes, 2024

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:24.971717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e6b4b613-f5ef-46f7-bbae-b0855909a90c · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Swin transformer: Hierarchical vision transformer using shifted windows

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:24.955671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d1446b60-30b7-4cc9-bb6c-cda591836578 · outbound

This paper cites Swin Transformer V2: Scaling Up Capacity and Resolution.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Swin Transformer V2: Scaling Up Capacity and Resolution

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-10T23:01:24.451942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec86c907-b158-4732-a2f0-d70052d5dea1 · outbound

This paper cites A ConvNet for the 2020s.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries A ConvNet for the 2020s

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-10T23:01:24.456928Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e47aac08-cf2c-4006-939d-8b68380867cc · outbound

This paper cites Fourier neural operator for parametric partial differential equations, 2021.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Fourier neural operator for parametric partial differential equations, 2021

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-10T23:01:24.462108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c584e4c-9edc-4b72-a373-3b74d0157fbc · outbound

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

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Convolutional neural operators for robust and accurate learning of pdes, 2023

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:24.929150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 700ed6c9-827f-4b23-a942-d7c83241e2c5 · outbound

This paper cites Wavelet neural operator for solving parametric partial differential equations in computational mechanics problems.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Wavelet neural operator for solving parametric partial differential equations in computational mechanics problems

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-10T23:01:24.471837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f9e8d12-bc33-4ee9-bfdf-dadd296c8a20 · outbound

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

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Learning nonlinear oper- ators via deeponet based on the universal approximation theorem of operators

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:24.913324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bae3c00e-7ed9-4e4b-afc3-542bd10d5446 · outbound

This paper cites Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:01:24.894341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b9c6276f-e55d-4154-a7f0-c47e8fb322f1 · outbound

This paper cites Geom-deeponet: A point-cloud-based deep operator network for field predictions on 3d parameterized geometries.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries Geom-deeponet: A point-cloud-based deep operator network for field predictions on 3d parameterized geometries

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-10T23:01:24.485534Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

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

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor cites this paper.

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

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:04:17.004774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.