Unsupervised Adaptation of PDE Foundation Models | Paper Citation Record

Observation f9f5c58b-cba0-40ed-9a50-c1de24d8c675 · outbound

This paper cites One-shot transfer learning for nonlinear pdes with perturbative pinns.arXiv preprint arXiv:2511.11137, 2025.

Unsupervised Adaptation of PDE Foundation Models One-shot transfer learning for nonlinear pdes with perturbative pinns.arXiv preprint arXiv:2511.11137, 2025

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:11.403340Z

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Observation 054a182f-c88f-4743-bcaa-af80fd1e6d8f · outbound

This paper cites A table of solutions of the one-dimensional burgers equation.Quarterly of Applied Mathematics, 30(2):195–212, 1972.

Unsupervised Adaptation of PDE Foundation Models A table of solutions of the one-dimensional burgers equation.Quarterly of Applied Mathematics, 30(2):195–212, 1972

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:12.785956Z

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Observation d4d975af-dd37-4140-b6b3-5cf30dd2a1f5 · outbound

This paper cites Hypino: Multi-physics neural operators via hyperpinns and the method of manufactured solu- tions.arXiv preprint arXiv:2509.05117, 2025.

Unsupervised Adaptation of PDE Foundation Models Hypino: Multi-physics neural operators via hyperpinns and the method of manufactured solu- tions.arXiv preprint arXiv:2509.05117, 2025

Reference 3

Resolution: verified exact; raw_fallback, observed 2026-08-10T15:41:12.392740Z

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Observation 29689080-43d6-467c-a63a-4c47a6e7a680 · outbound

This paper cites an unresolved cited work.

Unsupervised Adaptation of PDE Foundation Models Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-10T15:41:12.774739Z

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Observation 008e72c1-7785-4409-bb45-07acf921bbcf · outbound

This paper cites Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020.

Unsupervised Adaptation of PDE Foundation Models Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:11.419796Z

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Observation 228efd8e-1854-40d3-91c4-37031d928d2b · outbound

This paper cites OLoRA: Orthonormal Low-Rank Adaptation of Large Language Models.

Unsupervised Adaptation of PDE Foundation Models OLoRA: Orthonormal Low-Rank Adaptation of Large Language Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:11.423934Z

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Observation 22e877a0-20d7-4536-be4c-55b3cb63b2db · outbound

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

Unsupervised Adaptation of PDE Foundation Models Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems.IEEE Trans

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:11.428373Z

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Observation 0084048c-a055-4e76-bf12-f7c95e953555 · outbound

This paper cites OmniArch: Building Foundation Model For Scientific Computing.

Unsupervised Adaptation of PDE Foundation Models OmniArch: Building Foundation Model For Scientific Computing

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:11.432996Z

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Observation f4e0178f-e5c4-469d-a313-58fbcc18de17 · outbound

This paper cites Can-pinn: A fast physics-informed neural network based on coupled-automatic–numerical differentiation method.Computer Methods in Applied Mechanics and Engineering, 395:114909, 2022.

Unsupervised Adaptation of PDE Foundation Models Can-pinn: A fast physics-informed neural network based on coupled-automatic–numerical differentiation method.Computer Methods in Applied Mechanics and Engineering, 395:114909, 2022

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:12.757074Z

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Observation 355ed45d-989a-4db7-aa7c-92a8803c1bed · outbound

This paper cites On a quasi-linear parabolic equation occurring in aerodynamics.Quarterly of applied mathematics, 9(3):225–236, 1951.

Unsupervised Adaptation of PDE Foundation Models On a quasi-linear parabolic equation occurring in aerodynamics.Quarterly of applied mathematics, 9(3):225–236, 1951

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:12.746620Z

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Observation 876234f3-8224-41b8-9647-4c6d662edf68 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

Unsupervised Adaptation of PDE Foundation Models Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:11.444567Z

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Observation 6bbb08ca-afc0-4d0c-8ef4-c88e1bb74f6c · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Unsupervised Adaptation of PDE Foundation Models An image is worth 16x16 words: Transformers for image recognition at scale

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:11.447915Z

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Observation 7f8032c8-be9d-444a-8627-17d0b3526370 · outbound

This paper cites Evans.Partial differential equations, volume 19 ofGrad.

Unsupervised Adaptation of PDE Foundation Models Evans.Partial differential equations, volume 19 ofGrad

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:12.722433Z

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Observation 87378065-af29-470c-9b6c-7921f1c49bff · outbound

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

Unsupervised Adaptation of PDE Foundation Models Towards Multi-spatiotemporal-scale Generalized PDE Modeling

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:11.454691Z

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Observation 48575b25-c530-4abc-944e-eaeae3d16d01 · outbound

This paper cites DPOT: auto-regressive denoising operator transformer for large-scale PDE pre-training.

Unsupervised Adaptation of PDE Foundation Models DPOT: auto-regressive denoising operator transformer for large-scale PDE pre-training

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:12.710772Z

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Observation dd4a243c-c875-4877-b4f8-926a8bafd932 · outbound

This paper cites Neighborhood attention transformer.

Unsupervised Adaptation of PDE Foundation Models Neighborhood attention transformer

Reference 16

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-10T15:41:12.289886Z

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Observation 211ffb5f-af78-45c5-b4f0-22eb1e90a79f · outbound

This paper cites Poseidon: Efficient founda- tion models for pdes.

Unsupervised Adaptation of PDE Foundation Models Poseidon: Efficient founda- tion models for pdes

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:12.699928Z

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Observation 60eec8d7-f3be-4010-a289-e7f16dd3f58f · outbound

This paper cites Holzschuh, Qiang Liu, Georg Kohl, and Nils Thuerey.

Unsupervised Adaptation of PDE Foundation Models Holzschuh, Qiang Liu, Georg Kohl, and Nils Thuerey

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:12.689009Z

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Observation 2ccf1239-3fb0-4dfe-828d-14260484c6ba · outbound

This paper cites The partial differential equation ut +uu x =µu xx.Communications on Pure and Applied Mathematics, 3(3):201–230, 1950.

Unsupervised Adaptation of PDE Foundation Models The partial differential equation ut +uu x =µu xx.Communications on Pure and Applied Mathematics, 3(3):201–230, 1950

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:11.472973Z

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Observation 2a561bcd-32c9-425e-b930-94a926aa4b7c · outbound

This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.

Unsupervised Adaptation of PDE Foundation Models Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:11.476430Z

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Observation 88d88317-3a2c-4099-82c7-5bf17511193e · outbound

This paper cites Muon: An optimizer for hidden layers in neural networks, 2024.

Unsupervised Adaptation of PDE Foundation Models Muon: An optimizer for hidden layers in neural networks, 2024

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:11.483216Z

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Observation c0edaffa-009d-4755-b616-aa353dc4c4d5 · outbound

This paper cites Uniform spec- tral growth and convergence of muon in lora-style matrix factorization.arXiv preprint arXiv:2602.06385, 2026.

Unsupervised Adaptation of PDE Foundation Models Uniform spec- tral growth and convergence of muon in lora-style matrix factorization.arXiv preprint arXiv:2602.06385, 2026

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:11.486539Z

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Observation 48d1594b-f558-4c2b-a173-cca869e829e4 · outbound

This paper cites Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang.

Unsupervised Adaptation of PDE Foundation Models Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:11.489805Z

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Observation d09da347-c4b0-4286-8484-ebf31307cd36 · outbound

This paper cites Apebench: A benchmark for autoregressive neural emulators of pdes.

Unsupervised Adaptation of PDE Foundation Models Apebench: A benchmark for autoregressive neural emulators of pdes

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:12.651730Z

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Observation 8bccf7a7-027d-4bdf-a048-42478e38b5ae · outbound

This paper cites Multi-Grid Tensorized Fourier Neural Operator for High-Resolution PDEs.

Unsupervised Adaptation of PDE Foundation Models Multi-Grid Tensorized Fourier Neural Operator for High-Resolution PDEs

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:11.495798Z

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Observation 56737f3c-09a3-47cb-acf3-345e2c86706b · outbound

This paper cites Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew M.

Unsupervised Adaptation of PDE Foundation Models Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew M

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:12.640847Z

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Observation ee9baa7e-f92e-43ab-8e8f-a500d8cb0818 · outbound

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

Unsupervised Adaptation of PDE Foundation Models LeVeque.Finite Difference Methods for Ordinary and Partial Differential Equations: Steady-State and Time-Dependent Problems

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:12.630146Z

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Observation ef000df0-f9c3-4443-bf19-d81607c95dc4 · outbound

This paper cites Stuart, and Anima Anandkumar.

Unsupervised Adaptation of PDE Foundation Models Stuart, and Anima Anandkumar

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:12.619460Z

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Observation 93f85bf0-a874-4089-9d1e-1d84bf46fb0c · outbound

This paper cites Fourier neural operator with learned deformations for pdes on general geometries.Journal of Machine Learning Research, 24(388):1–26, 2023.

Unsupervised Adaptation of PDE Foundation Models Fourier neural operator with learned deformations for pdes on general geometries.Journal of Machine Learning Research, 24(388):1–26, 2023

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:11.509804Z

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Observation 3e20bee4-591c-4841-8492-4b9702df231b · outbound

This paper cites Physics-informed neural operator for learning partial differential equations.ACM/IMS Journal of Data Science, 1(3):1–27, 2024.

Unsupervised Adaptation of PDE Foundation Models Physics-informed neural operator for learning partial differential equations.ACM/IMS Journal of Data Science, 1(3):1–27, 2024

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Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:11.513090Z

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Observation f03e8c87-2926-486c-a874-ce1ee6438198 · outbound

This paper cites A convnet for the 2020s.

Unsupervised Adaptation of PDE Foundation Models A convnet for the 2020s

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Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:11.516396Z

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Observation d226f9d6-9a0c-4aa1-9ce8-4bb0b8a11ace · outbound

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

Unsupervised Adaptation of PDE Foundation Models Learning nonlinear operators via deeponet based on the universal approximation theorem of operators

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Observation 4326d286-7448-4417-b141-0d54ec23bdf6 · outbound

This paper cites Cranmer, Alberto Bietti, Michael Eickenberg, Siavash Golkar, Géraud Krawezik, François Lanusse, Mariel Pettee, Tiberiu Tesileanu, Kyunghyun Cho, and Shirley Ho.

Unsupervised Adaptation of PDE Foundation Models Cranmer, Alberto Bietti, Michael Eickenberg, Siavash Golkar, Géraud Krawezik, François Lanusse, Mariel Pettee, Tiberiu Tesileanu, Kyunghyun Cho, and Shirley Ho

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:12.589761Z

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Observation c21f3197-dfa2-4b28-b7aa-18d32c8f7428 · outbound

This paper cites PhysiX: A Foundation Model for Physics Simulations.

Unsupervised Adaptation of PDE Foundation Models PhysiX: A Foundation Model for Physics Simulations

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Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:11.527694Z

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Observation 28574b0b-cad5-4547-8c1e-942e8c05a6e6 · outbound

This paper cites The well: a large-scale collection of diverse physics simulations for machine learning.Advances in Neural Information Processing Systems, 37:44989–45037, 2024.

Unsupervised Adaptation of PDE Foundation Models The well: a large-scale collection of diverse physics simulations for machine learning.Advances in Neural Information Processing Systems, 37:44989–45037, 2024

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Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:11.531718Z

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Observation 58737e92-604a-4c1a-b700-f087d5e9237c · outbound

This paper cites Karniadakis.

Unsupervised Adaptation of PDE Foundation Models Karniadakis

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:11.535432Z

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Observation c5f693d4-3ce5-4c2c-91d0-a4ce86d67c38 · outbound

This paper cites Convolutional neural operators for robust and accurate learning of PDEs.

Unsupervised Adaptation of PDE Foundation Models Convolutional neural operators for robust and accurate learning of PDEs

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:12.573080Z

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Observation 37abe6ac-55cb-4781-b0fe-509757836a37 · outbound

This paper cites Morph: Pde foundation models with arbitrary data modality.arXiv preprint arXiv:2509.21670, 2025.

Unsupervised Adaptation of PDE Foundation Models Morph: Pde foundation models with arbitrary data modality.arXiv preprint arXiv:2509.21670, 2025

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Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:11.543938Z

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Observation c08a3cd0-c18f-451a-b4ea-024139acbdb3 · outbound

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

Unsupervised Adaptation of PDE Foundation Models U-net: Convolutional networks for biomedical image segmentation

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Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:11.547786Z

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Observation 0d9c9b56-4387-4b3e-9d3d-235218333ad7 · outbound

This paper cites Test-time gen- eralization for physics through neural operator splitting.arXiv preprint arXiv:2602.00884, 2026.

Unsupervised Adaptation of PDE Foundation Models Test-time gen- eralization for physics through neural operator splitting.arXiv preprint arXiv:2602.00884, 2026

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Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:11.551909Z

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Observation 86173532-bd1d-45e9-bd76-5b3254a4b138 · outbound

This paper cites GLU Variants Improve Transformer.

Unsupervised Adaptation of PDE Foundation Models GLU Variants Improve Transformer

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Observation 090144e5-d89c-4145-bbac-046d2f312e01 · outbound

This paper cites LeMON: Learning to Learn Multi-Operator Networks.

Unsupervised Adaptation of PDE Foundation Models LeMON: Learning to Learn Multi-Operator Networks

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Observation 0175ab15-4375-413a-ace1-49f2baceead0 · outbound

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

Unsupervised Adaptation of PDE Foundation Models Pdebench: An extensive benchmark for scientific machine learning

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:12.556985Z

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Observation 6e16f053-28b3-4549-97fd-1d565089be52 · outbound

This paper cites Mechanism of the production of small eddies from large ones.Proceedings of the Royal Society of London.

Unsupervised Adaptation of PDE Foundation Models Mechanism of the production of small eddies from large ones.Proceedings of the Royal Society of London

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Observation 533230cc-7054-43a7-9df6-50a0e840c633 · outbound

This paper cites Factorized Fourier Neural Operators.

Unsupervised Adaptation of PDE Foundation Models Factorized Fourier Neural Operators

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Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:11.571942Z

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Observation 780b51a8-5b6e-48d2-9a58-414c574fde8f · outbound

This paper cites Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets.

Unsupervised Adaptation of PDE Foundation Models Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets

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Observation 73460f66-a39b-4ea9-9578-6883b5e14cc7 · outbound

This paper cites Respecting causality is all you need for training physics-informed neural networks.

Unsupervised Adaptation of PDE Foundation Models Respecting causality is all you need for training physics-informed neural networks

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Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:11.580063Z

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Observation c1cd3d4f-5620-4f8c-975e-5ca48f4db6cb · outbound

This paper cites When and why pinns fail to train: A neural tangent kernel perspective.J.

Unsupervised Adaptation of PDE Foundation Models When and why pinns fail to train: A neural tangent kernel perspective.J

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Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:11.584064Z

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Observation 5603482c-4e02-49d9-9bea-dc25d9dcf156 · outbound

This paper cites Gradient alignment in physics-informed neural networks: A second-order optimization perspective.

Unsupervised Adaptation of PDE Foundation Models Gradient alignment in physics-informed neural networks: A second-order optimization perspective

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:12.540532Z

Source-reported events for the cited work

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Observation eba76cf4-8214-4b9d-bc3f-4a843cd18ebb · outbound

This paper cites Orthogonal subspace learning for language model continual learning.

Unsupervised Adaptation of PDE Foundation Models Orthogonal subspace learning for language model continual learning

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:11.591879Z

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Observation 89998280-e55f-4025-88e0-e6492ab9ec9f · outbound

This paper cites Orthogeolora: Geometric parameter-efficient fine-tuning for structured social science concept retrieval on theweb.arXiv preprint arXiv:2601.09185, 2026.

Unsupervised Adaptation of PDE Foundation Models Orthogeolora: Geometric parameter-efficient fine-tuning for structured social science concept retrieval on theweb.arXiv preprint arXiv:2601.09185, 2026

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Resolution: verified exact; raw_fallback, observed 2026-08-10T15:41:11.911176Z

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Observation 1eb6d308-e42c-42a6-9bb0-ce6f06c51ace · outbound

This paper cites Evolutionary neural architecture search for physics-informed neural networks with variable-length designs.IEEE Transactions on Evolutionary Computation, pages 1–1, 2026.

Unsupervised Adaptation of PDE Foundation Models Evolutionary neural architecture search for physics-informed neural networks with variable-length designs.IEEE Transactions on Evolutionary Computation, pages 1–1, 2026

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:12.522886Z

Source-reported events for the cited work

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Observation 5753a787-e925-4110-8d88-894cc226102e · outbound

This paper cites Out-of-distribution generalization for neural physics solvers.arXiv preprint arXiv:2601.19091, 2026.

Unsupervised Adaptation of PDE Foundation Models Out-of-distribution generalization for neural physics solvers.arXiv preprint arXiv:2601.19091, 2026

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Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:11.602580Z

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Observation cc694545-04fb-4ea2-b7f1-5400db081394 · outbound

This paper cites Geometry aware operator transformer as an efficient and accurate neural surrogate for PDEs on arbitrary domains.

Unsupervised Adaptation of PDE Foundation Models Geometry aware operator transformer as an efficient and accurate neural surrogate for PDEs on arbitrary domains

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:12.512021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a47ec9d9-4955-41e8-ac69-0acf710f343a · outbound

This paper cites Transolver: A fast transformer solver for pdes on general geometries.

Unsupervised Adaptation of PDE Foundation Models Transolver: A fast transformer solver for pdes on general geometries

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:12.500392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b2b135aa-d949-4b61-9213-06d65cf46d99 · outbound

This paper cites Oplora: Orthogonal projection lora prevents catastrophic forgetting during parameter-efficient fine-tuning.

Unsupervised Adaptation of PDE Foundation Models Oplora: Orthogonal projection lora prevents catastrophic forgetting during parameter-efficient fine-tuning

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:11.612433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81145e23-2e90-4b12-9058-07803e7751fa · outbound

This paper cites Root mean square layer normalization.

Unsupervised Adaptation of PDE Foundation Models Root mean square layer normalization

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:12.482596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b85c55ae-183b-4cf3-b6ad-67effc627a48 · outbound

This paper cites Physics-informed temporal alignment for auto-regressive PDE foundation models.

Unsupervised Adaptation of PDE Foundation Models Physics-informed temporal alignment for auto-regressive PDE foundation models

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:12.469648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b6149cdb-f527-43c3-8d78-fafad3cec03e · outbound

This paper cites Width” denotes hidden_channels for FNO and TFNO and init_features for the two U-Nets. “Depth.

Unsupervised Adaptation of PDE Foundation Models Width” denotes hidden_channels for FNO and TFNO and init_features for the two U-Nets. “Depth

Reference 59

Resolution: malformed identifier; no resolver link, observed 2026-08-10T15:41:11.622295Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 439202b6-1fc9-4a3c-8335-533cf2b9d602 · outbound

This paper cites URLhttps://openreview.net/forum?id=nZeVKeeFYf9.

Unsupervised Adaptation of PDE Foundation Models URLhttps://openreview.net/forum?id=nZeVKeeFYf9

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:11.479859Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers