Large language models for partial differential equation workflows | Paper Citation Record

Observation 34a98559-151c-4dfa-8f66-13ccb354ab7f · outbound

This paper cites Evans.Partial Differential Equations, volume 19 ofGraduate Studies in Mathe- matics.

Large language models for partial differential equation workflows Evans.Partial Differential Equations, volume 19 ofGraduate Studies in Mathe- matics

Reference 1

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Large language models for partial differential equation workflows Unresolved cited work

Reference 2

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This paper cites Oberkampf and Timothy G.

Large language models for partial differential equation workflows Oberkampf and Timothy G

Reference 3

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This paper cites Prentice Hall, Upper Saddle River, NJ, 2 edition, 1999.

Large language models for partial differential equation workflows Prentice Hall, Upper Saddle River, NJ, 2 edition, 1999

Reference 4

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Observation e809e583-7bb2-4a2f-896a-029e527fa3f7 · outbound

This paper cites LeVeque.Finite Difference Methods for Ordinary and Partial Differential Equations: Steady-State and Time-Dependent Problems.

Large language models for partial differential equation workflows LeVeque.Finite Difference Methods for Ordinary and Partial Differential Equations: Steady-State and Time-Dependent Problems

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Large language models for partial differential equation workflows Unresolved cited work

Reference 6

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Observation 671728f8-7ad9-4916-89f9-0ba80d85b12e · outbound

This paper cites Brenner and L.

Large language models for partial differential equation workflows Brenner and L

Reference 7

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Observation 742e39ea-51be-479e-9bc8-6e4871cca775 · outbound

This paper cites Trefethen.Spectral Methods in MATLAB.

Large language models for partial differential equation workflows Trefethen.Spectral Methods in MATLAB

Reference 8

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Observation 32e1e83c-e8b0-456c-941f-03703802e7be · outbound

This paper cites Review of discontinuous galerkin finite element methods for partial differential equations on complicated domains.

Large language models for partial differential equation workflows Review of discontinuous galerkin finite element methods for partial differential equations on complicated domains

Reference 9

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This paper cites A review of mesh adaptation technology applied to computational fluid dynamics.Fluids, 10(5):129, 2025.

Large language models for partial differential equation workflows A review of mesh adaptation technology applied to computational fluid dynamics.Fluids, 10(5):129, 2025

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Observation 09346225-dfea-4b2c-a292-6afb35bd6268 · outbound

This paper cites Springer, Berlin, Heidelberg, 1971.

Large language models for partial differential equation workflows Springer, Berlin, Heidelberg, 1971

Reference 11

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Observation 83ce9f33-270a-4ba3-8b11-30f10f964e76 · outbound

This paper cites Springer, Dor- drecht, 2009.

Large language models for partial differential equation workflows Springer, Dor- drecht, 2009

Reference 12

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Observation 7989a46f-6778-4773-962a-0856228739a7 · outbound

This paper cites Gunzburger.Perspectives in Flow Control and Optimization.

Large language models for partial differential equation workflows Gunzburger.Perspectives in Flow Control and Optimization

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:19:05.984497Z

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Observation d5e1172a-f6f5-4ddc-8355-6bd9a8d75ebf · outbound

This paper cites Bendsøe and Ole Sigmund.Topology Optimization: Theory, Methods, and Applica- tions.

Large language models for partial differential equation workflows Bendsøe and Ole Sigmund.Topology Optimization: Theory, Methods, and Applica- tions

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Observation f93466f6-5816-4ec0-a0a4-7c6beb0c5380 · outbound

This paper cites Brunton, Joshua L.

Large language models for partial differential equation workflows Brunton, Joshua L

Reference 15

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Observation 06e7c1a8-9326-4867-8e19-0dfd58e94171 · outbound

This paper cites Rudy, Steven L.

Large language models for partial differential equation workflows Rudy, Steven L

Reference 16

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Observation 705900b0-b8d9-4ca3-9da9-02a1df26dc09 · outbound

This paper cites Data-driven equation discovery of ocean mesoscale closures.

Large language models for partial differential equation workflows Data-driven equation discovery of ocean mesoscale closures

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:19:05.603649Z

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Observation a140e736-fafe-4954-9018-0a7e2f3e00b6 · outbound

This paper cites Formulating turbulence closures using sparse regression with embedded form invariance.Physical Review Fluids, 5(8):084611, 2020.

Large language models for partial differential equation workflows Formulating turbulence closures using sparse regression with embedded form invariance.Physical Review Fluids, 5(8):084611, 2020

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:19:05.387241Z

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Observation 343f0245-c043-402a-bd6a-84a838c4afb0 · outbound

This paper cites Data-driven discovery of coarse-grained equations.

Large language models for partial differential equation workflows Data-driven discovery of coarse-grained equations

Reference 19

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Large language models for partial differential equation workflows Unresolved cited work

Reference 20

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Large language models for partial differential equation workflows Unresolved cited work

Reference 21

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Observation 5d631dab-8dab-42a3-a68b-e7ddf57c1d6b · outbound

This paper cites Smith, Ayya Alieva, Qing Wang, Michael P.

Large language models for partial differential equation workflows Smith, Ayya Alieva, Qing Wang, Michael P

Reference 22

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Observation c6d342bd-e9d8-4d92-ad2a-b8256d8d8efe · outbound

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

Large language models for partial differential equation workflows Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators

Reference 23

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Observation a3b5042f-c961-4a86-b0e0-4fe440939b41 · outbound

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

Large language models for partial differential equation workflows Fourier neural operator for parametric partial differential equations

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:19:04.546189Z

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Observation edb3df40-3dcb-4de5-a1e2-c0a3a313be58 · outbound

This paper cites Factorized Fourier neural operators.

Large language models for partial differential equation workflows Factorized Fourier neural operators

Reference 25

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Observation f933571a-056d-4c31-9fda-3b61c0ed073b · outbound

This paper cites Physics-informed neural networks with hard constraints for inverse design.SIAM Journal on Scientific Computing, 43(6):B1105–B1132, 2021.

Large language models for partial differential equation workflows Physics-informed neural networks with hard constraints for inverse design.SIAM Journal on Scientific Computing, 43(6):B1105–B1132, 2021

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-05T16:18:46.741318Z

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Observation d042fb22-248d-4f37-8f80-4a2351466b3b · outbound

This paper cites Gradient-enhanced physics- informed neural networks for forward and inverse pde problems.Computer Methods in Applied Mechanics and Engineering, 393:114823, 2022.

Large language models for partial differential equation workflows Gradient-enhanced physics- informed neural networks for forward and inverse pde problems.Computer Methods in Applied Mechanics and Engineering, 393:114823, 2022

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:19:04.383908Z

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Observation 16144691-e3de-40f3-8640-99c0f8dfdf55 · outbound

This paper cites Encoding physics to learn reaction–diffusion processes.Nature Machine Intelligence, 5(7):765–779, 2023.

Large language models for partial differential equation workflows Encoding physics to learn reaction–diffusion processes.Nature Machine Intelligence, 5(7):765–779, 2023

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:19:04.168736Z

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Observation c2302bc5-5e3d-4e3f-a44f-9b60d2896a59 · outbound

This paper cites Pesanet: Physics-encoded spec- tral attention network for simulating pde-governed complex systems.

Large language models for partial differential equation workflows Pesanet: Physics-encoded spec- tral attention network for simulating pde-governed complex systems

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Observation ec3874a6-5be9-48f7-9b3b-0e97307c03a2 · outbound

This paper cites Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control.Journal of Fluid Mechanics, 865:281–302, 2019.

Large language models for partial differential equation workflows Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control.Journal of Fluid Mechanics, 865:281–302, 2019

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:19:03.710999Z

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Observation 0cefd96f-fdc8-41f8-b60c-51f6e97b8fd0 · outbound

This paper cites Learning to control pdes with differentiable physics, 2020.

Large language models for partial differential equation workflows Learning to control pdes with differentiable physics, 2020

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:19:03.502339Z

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Observation ee71c12d-7dd6-44be-80bd-bc56edac9bab · outbound

This paper cites Direct shape optimization through deep reinforcement learning.Journal of Computational Physics, 428:110080, 2021.

Large language models for partial differential equation workflows Direct shape optimization through deep reinforcement learning.Journal of Computational Physics, 428:110080, 2021

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:19:03.270362Z

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This paper cites Stachenfeld, Alvaro Sanchez-Gonzalez, Pe- ter Battaglia, Jessica B.

Large language models for partial differential equation workflows Stachenfeld, Alvaro Sanchez-Gonzalez, Pe- ter Battaglia, Jessica B

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Observation a2ff970f-266b-496a-956c-a35ecb0d67d4 · outbound

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Large language models for partial differential equation workflows Unresolved cited work

Reference 34

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Observation 94cb1065-c665-4d57-97fc-2cb837a19c3a · outbound

This paper cites Chi, Quoc V.

Large language models for partial differential equation workflows Chi, Quoc V

Reference 35

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Observation 01643b77-3029-4f91-acf1-e739d3af753b · outbound

This paper cites MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning.

Large language models for partial differential equation workflows MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning

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Observation 688a5ed4-f226-4236-9d76-045dcf29d938 · outbound

This paper cites LLM4ED: Large Language Models for Automatic Equation Discovery.

Large language models for partial differential equation workflows LLM4ED: Large Language Models for Automatic Equation Discovery

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Observation 63ebc101-381a-42f4-86ba-4acb061ec525 · outbound

This paper cites Physpde: Rethinking pde discovery and a physical hypothesis selection benchmark.

Large language models for partial differential equation workflows Physpde: Rethinking pde discovery and a physical hypothesis selection benchmark

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:19:02.891730Z

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Observation a7858813-6387-49d6-b686-8dec5806e8f1 · outbound

This paper cites Codepde: An inference framework for llm-driven pde solver generation.

Large language models for partial differential equation workflows Codepde: An inference framework for llm-driven pde solver generation

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:19:02.677029Z

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Observation 1b62f2ad-3648-45fb-9175-0c0f67af63d8 · outbound

This paper cites Foam-agent: Towards automated intelligent cfd workflows.

Large language models for partial differential equation workflows Foam-agent: Towards automated intelligent cfd workflows

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:19:02.486055Z

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Observation e706ab58-f934-4d50-9c4e-2db7181f7615 · outbound

This paper cites Pde-sharp: Pde solver hybrids through analysis and refinement passes.arXiv preprint arXiv:2511.00183, 2025.

Large language models for partial differential equation workflows Pde-sharp: Pde solver hybrids through analysis and refinement passes.arXiv preprint arXiv:2511.00183, 2025

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Observation 2921c93a-d007-4e4f-93e0-f3270f651e7b · outbound

This paper cites Pde-controller: Llms for autoformalization and reasoning of pdes.

Large language models for partial differential equation workflows Pde-controller: Llms for autoformalization and reasoning of pdes

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:19:02.346579Z

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Observation f6c51466-8128-464f-8736-fd0ab89b7c3a · outbound

This paper cites Using large language models for parametric shape op- timization.Physics of Fluids, 37(8):083601, 2025.

Large language models for partial differential equation workflows Using large language models for parametric shape op- timization.Physics of Fluids, 37(8):083601, 2025

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:19:02.195485Z

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Observation ffb367f9-d793-4aa4-842e-8bac8fce8f6c · outbound

This paper cites Accelerating scientific discovery with co-scientist.Nature, 655:487–496, 2026.

Large language models for partial differential equation workflows Accelerating scientific discovery with co-scientist.Nature, 655:487–496, 2026

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:19:01.994569Z

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Observation 7968a697-c3a8-4b73-a20f-9b668e78deb1 · outbound

This paper cites Ghareeb, Benjamin Chang, Ludovico Mitchener, Angela Yiu, Caralyn J.

Large language models for partial differential equation workflows Ghareeb, Benjamin Chang, Ludovico Mitchener, Angela Yiu, Caralyn J

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:19:01.845941Z

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Observation baeedead-a9f3-4c8e-aab2-9f0d80a420f3 · outbound

This paper cites Evaluating llms’ divergent thinking capabilities for scientific idea generation with minimal context.Nature Communications, 17(1):3625, 2026.

Large language models for partial differential equation workflows Evaluating llms’ divergent thinking capabilities for scientific idea generation with minimal context.Nature Communications, 17(1):3625, 2026

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:19:01.664646Z

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Observation 5c22b411-ffe8-462e-ba35-3fc2d110ef59 · outbound

This paper cites Llm assisted mathematical modeling: Homogeneous laplace equation in cylinder with the complete electrode model.

Large language models for partial differential equation workflows Llm assisted mathematical modeling: Homogeneous laplace equation in cylinder with the complete electrode model

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:19:01.459853Z

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Observation f93f45a1-85d3-4251-939c-f817a516c307 · outbound

This paper cites Agentic symbolic search: Characterizing pdes beyond hand-crafted expressions, meshes, and neural networks, 2026.

Large language models for partial differential equation workflows Agentic symbolic search: Characterizing pdes beyond hand-crafted expressions, meshes, and neural networks, 2026

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:19:01.268100Z

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Observation 2a2c1f0f-5198-4c17-982f-66129aea1fee · outbound

This paper cites The impact of large language models on scientific discovery: a preliminary study using gpt-4.

Large language models for partial differential equation workflows The impact of large language models on scientific discovery: a preliminary study using gpt-4

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:19:01.083668Z

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Observation 87620b2a-cf1a-47af-b059-edb00896f0c1 · outbound

This paper cites DrSR: LLM based Scientific Equation Discovery with Dual Reasoning from Data and Experience.

Large language models for partial differential equation workflows DrSR: LLM based Scientific Equation Discovery with Dual Reasoning from Data and Experience

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Observation e4012e50-6705-4548-b592-a4fad9b928bb · outbound

This paper cites LLM-SR: Scientific Equation Discovery via Programming with Large Language Models.

Large language models for partial differential equation workflows LLM-SR: Scientific Equation Discovery via Programming with Large Language Models

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Observation f9ff3fa7-ee2f-429c-be39-52ded5a6a942 · outbound

This paper cites From equations to insights: Unraveling symbolic structures in pdes with llms.arXiv preprint arXiv:2503.09986, 2025.

Large language models for partial differential equation workflows From equations to insights: Unraveling symbolic structures in pdes with llms.arXiv preprint arXiv:2503.09986, 2025

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Resolution: verified exact; raw_fallback, observed 2026-08-05T16:18:53.311247Z

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Observation 8a067c7b-22d9-4e8b-ba7a-298d8bfeabd0 · outbound

This paper cites LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery.

Large language models for partial differential equation workflows LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery

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Observation e5313d3e-2170-455f-91d5-7c3ecfc94ce5 · outbound

This paper cites an unresolved cited work.

Large language models for partial differential equation workflows Unresolved cited work

Reference 54

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Observation 9ca5f8e2-ed19-4342-89c5-67027f22a110 · outbound

This paper cites Pdeagent-bench: A multi-metric, multi-library benchmark for pde solver generation, 2026.

Large language models for partial differential equation workflows Pdeagent-bench: A multi-metric, multi-library benchmark for pde solver generation, 2026

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:19:00.694053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fd0e4df3-e0d3-49e6-b851-6b07374b4f8f · outbound

This paper cites Deepseek vs.

Large language models for partial differential equation workflows Deepseek vs

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:19:00.467164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e91a6190-4104-4b26-90f7-b33ca2c6cf0c · outbound

This paper cites All-fem: Agentic large language models fine-tuned for 24 finite element methods.Computer Methods in Applied Mechanics and Engineering, 457:118985, 2026.

Large language models for partial differential equation workflows All-fem: Agentic large language models fine-tuned for 24 finite element methods.Computer Methods in Applied Mechanics and Engineering, 457:118985, 2026

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:19:00.344218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation eeabfe76-604d-4e18-ba05-171304c0bfb1 · outbound

This paper cites Automated code development for pde solvers using large language models.

Large language models for partial differential equation workflows Automated code development for pde solvers using large language models

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:19:00.069748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 94e64bfa-fc25-4367-a9d1-cb975e87a4a9 · outbound

This paper cites Autonumerics: An autonomous, pde-agnostic multi-agent pipeline for scientific computing, 2026.

Large language models for partial differential equation workflows Autonumerics: An autonomous, pde-agnostic multi-agent pipeline for scientific computing, 2026

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:59.896749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fa04a730-0e79-4b78-9e5a-3346f4f2ecdd · outbound

This paper cites Evaluations of large language models in computa- tional fluid dynamics: Leveraging, learning and creating knowledge.Theoretical and Applied Mechanics Letters, 15(3):100597, 2025.

Large language models for partial differential equation workflows Evaluations of large language models in computa- tional fluid dynamics: Leveraging, learning and creating knowledge.Theoretical and Applied Mechanics Letters, 15(3):100597, 2025

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:59.670106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 03e57cfc-9a0f-4b7b-babd-96a292c242c6 · outbound

This paper cites Cfdllmbench: A benchmark suite for evaluating large language models in computational fluid dynamics.

Large language models for partial differential equation workflows Cfdllmbench: A benchmark suite for evaluating large language models in computational fluid dynamics

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:59.444667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 903dd180-fd50-452f-86e8-ae674cd66747 · outbound

This paper cites Openfoamgpt: A retrieval-augmented large language model (llm) agent for openfoam-based computational fluid dynamics.Physics of Fluids, 37(3), 2025.

Large language models for partial differential equation workflows Openfoamgpt: A retrieval-augmented large language model (llm) agent for openfoam-based computational fluid dynamics.Physics of Fluids, 37(3), 2025

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-05T16:18:50.138829Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69fe66e0-0f21-45c7-b2cf-b391c7fa3dc4 · outbound

This paper cites Ai cfd scientist: Toward open-ended computational fluid dynamics discovery with physics-aware ai agents, 2026.

Large language models for partial differential equation workflows Ai cfd scientist: Toward open-ended computational fluid dynamics discovery with physics-aware ai agents, 2026

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:59.269734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1cf628ea-6f62-482a-9cdd-decf5c627b93 · outbound

This paper cites Openfoamgpt 2.0: End-to-end, trustworthy automation for computational fluid dynamics.International Journal of Heat and Fluid Flow, 120:110399, 2026.

Large language models for partial differential equation workflows Openfoamgpt 2.0: End-to-end, trustworthy automation for computational fluid dynamics.International Journal of Heat and Fluid Flow, 120:110399, 2026

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:59.040847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 13569cd7-bf2a-4330-9f04-38dcb19e6936 · outbound

This paper cites Metaopenfoam: an llm-based multi-agent framework for cfd.

Large language models for partial differential equation workflows Metaopenfoam: an llm-based multi-agent framework for cfd

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:58.836302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0bc953ed-07cc-427a-a39b-5e7ef071b777 · outbound

This paper cites Metaopenfoam 2.0: Large language model driven chain of thought for automating cfd simulation and post-processing.Journal Name, 2025.

Large language models for partial differential equation workflows Metaopenfoam 2.0: Large language model driven chain of thought for automating cfd simulation and post-processing.Journal Name, 2025

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:58.515670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 547f7227-6cdf-433c-b8d5-ac50d72d7b32 · outbound

This paper cites Chatcfd: An llm-driven agent for end-to-end cfd automation with domain-specific structured reasoning.

Large language models for partial differential equation workflows Chatcfd: An llm-driven agent for end-to-end cfd automation with domain-specific structured reasoning

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:58.189970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0f910363-9428-4298-95ed-8357aeaac612 · outbound

This paper cites Fine-tuning a large language model for automating com- putational fluid dynamics simulations.Theoretical and Applied Mechanics Letters, 15:100594, 2025.

Large language models for partial differential equation workflows Fine-tuning a large language model for automating com- putational fluid dynamics simulations.Theoretical and Applied Mechanics Letters, 15:100594, 2025

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:57.932209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 49e9db7a-ed9f-476c-9319-cfab14e856ae · outbound

This paper cites Physics simulation capabilities of llms.Physica Scripta, 99(11):116003, oct 2024.

Large language models for partial differential equation workflows Physics simulation capabilities of llms.Physica Scripta, 99(11):116003, oct 2024

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:57.539936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 236a9a0e-1f1a-4c77-a067-3bdd33a5e057 · outbound

This paper cites Mycrunchgpt: Achatgptassistedframeworkforscientificmachinelearning.Journal of Machine Learning for Modeling and Computing, 4(4):41–72, January 2023.

Large language models for partial differential equation workflows Mycrunchgpt: Achatgptassistedframeworkforscientificmachinelearning.Journal of Machine Learning for Modeling and Computing, 4(4):41–72, January 2023

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:57.323653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 455538be-7430-4df8-86fa-fbf6ac4152c0 · outbound

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

Large language models for partial differential equation workflows PINNsAgent: Automated PDE Surrogation with Large Language Models

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-05T16:18:51.098372Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5747acb2-9304-4dc4-9e47-d1722877ccce · outbound

This paper cites Lang-pinn: From language to physics-informed neural networks via a multi-agent framework.

Large language models for partial differential equation workflows Lang-pinn: From language to physics-informed neural networks via a multi-agent framework

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:57.163703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0086d22d-09aa-4000-8715-371b83c016f6 · outbound

This paper cites Text-trained llms can zero-shot extrapolate pde dynamics.arXiv preprint arXiv:2509.06322, 2025.

Large language models for partial differential equation workflows Text-trained llms can zero-shot extrapolate pde dynamics.arXiv preprint arXiv:2509.06322, 2025

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-05T16:18:51.249520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4aba927-5b7f-480c-8cca-e298aeca1816 · outbound

This paper cites Unisolver: Pde- conditional transformers towards universal neural pde solvers.

Large language models for partial differential equation workflows Unisolver: Pde- conditional transformers towards universal neural pde solvers

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:56.929600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a639cd34-ae5e-4074-9efd-a8ab7ee35ed6 · outbound

This paper cites UPS: Efficiently building foundation models for PDE solving via cross-modal adaptation.Transactions on Machine Learning Re- search, 2024.

Large language models for partial differential equation workflows UPS: Efficiently building foundation models for PDE solving via cross-modal adaptation.Transactions on Machine Learning Re- search, 2024

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:56.693449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c81484b8-6a00-4359-a6ca-242f2e052f02 · outbound

This paper cites Fluid-llm: Learning computational fluid dynamics with spatiotemporal-aware large language models.

Large language models for partial differential equation workflows Fluid-llm: Learning computational fluid dynamics with spatiotemporal-aware large language models

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:56.479753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7e0e2556-fe6e-418a-992f-066d32979b30 · outbound

This paper cites Buchanan, and Amir Barati Farimani.

Large language models for partial differential equation workflows Buchanan, and Amir Barati Farimani

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:56.255200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9cafb488-3202-4ee5-8e7d-fe8052741509 · outbound

This paper cites Yang, Zulfikhar A.

Large language models for partial differential equation workflows Yang, Zulfikhar A

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:56.058706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation dd340a43-ab67-46ea-9b66-d8d1103462d6 · outbound

This paper cites Aeroagent: A vision- physics-decision framework for aerodynamic vehicle design.

Large language models for partial differential equation workflows Aeroagent: A vision- physics-decision framework for aerodynamic vehicle design

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:55.815362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6f59649c-4161-4ae6-b0d0-82fcf923e5dc · outbound

This paper cites Shapebench: A scalable benchmark and diagnostic suite for standardized evaluation in aerodynamic shape optimization, 2026.

Large language models for partial differential equation workflows Shapebench: A scalable benchmark and diagnostic suite for standardized evaluation in aerodynamic shape optimization, 2026

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:55.579112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9ff55fb5-62cf-423c-a553-a8a956e838f2 · outbound

This paper cites Optmetaopenfoam: Large language model driven chain of thought for sensitivity analysis and parameter optimization based on cfd.Journal Name, 2025.

Large language models for partial differential equation workflows Optmetaopenfoam: Large language model driven chain of thought for sensitivity analysis and parameter optimization based on cfd.Journal Name, 2025

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:55.315190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ad476b8b-0045-4dba-9ff8-dfe54c64fd24 · outbound

This paper cites Self-evolving scientific agent discovers generalizable physically-reasoned fluid control, 2026.

Large language models for partial differential equation workflows Self-evolving scientific agent discovers generalizable physically-reasoned fluid control, 2026

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:55.183166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 53dc8168-9a24-494b-b282-6d35dcfb4d36 · outbound

This paper cites an unresolved cited work.

Large language models for partial differential equation workflows Unresolved cited work

Reference 83

Resolution: unresolved; raw_fallback, observed 2026-08-05T16:18:55.023199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6931d88c-f34c-4ad8-8abb-a9a4cbf7a99d · outbound

This paper cites Toward knowledge-guided ai for inverse design in manufacturing: A perspective on domain, physics, and human–ai synergy.

Large language models for partial differential equation workflows Toward knowledge-guided ai for inverse design in manufacturing: A perspective on domain, physics, and human–ai synergy

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:54.878610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 50de0bdf-5b50-4afd-b613-33320c091578 · outbound

This paper cites Toward autonomous engineering design: A knowledge-guided multi-agent framework, 2025.

Large language models for partial differential equation workflows Toward autonomous engineering design: A knowledge-guided multi-agent framework, 2025

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:54.694853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ed6c9d42-ed0b-4fee-9742-5f671e080e9f · outbound

This paper cites Think like a scientist: Physics-guided llm agent for equation discovery, 2026.

Large language models for partial differential equation workflows Think like a scientist: Physics-guided llm agent for equation discovery, 2026

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:54.511583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 77a8e1e8-4d1b-4e5a-90f3-66c1c95183dc · outbound

This paper cites Callaghan, and Dongxiao Zhang.

Large language models for partial differential equation workflows Callaghan, and Dongxiao Zhang

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:54.322809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f8f932cc-cda0-4ea0-b559-c23d2d5ff6f9 · outbound

This paper cites Osher, and Hayden Schaeffer.

Large language models for partial differential equation workflows Osher, and Hayden Schaeffer

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:54.154310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0f5d9b7b-91b0-4132-aa19-3e672cb763aa · outbound

This paper cites Jasak, A.

Large language models for partial differential equation workflows Jasak, A

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:53.974237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a00397e1-51b4-4ce8-a7a3-dd5d6610f228 · outbound

This paper cites PDEBench: AnExtensiveBenchmarkforScientificMachine Learning.

Large language models for partial differential equation workflows PDEBench: AnExtensiveBenchmarkforScientificMachine Learning

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:53.804186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f07c5078-2ba8-492e-84db-3a5ba6626006 · outbound

This paper cites Pinnacle: a comprehensive benchmark of physics-informed neural networks for solving pdes.

Large language models for partial differential equation workflows Pinnacle: a comprehensive benchmark of physics-informed neural networks for solving pdes

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:53.674753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers