Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs | Paper Citation Record

Observation b7241f5d-95f1-4322-be01-8c74119b037a · outbound

This paper cites Brunton and J.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Brunton and J

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.791905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ecbc515f-c0db-4f82-8c96-42120b0d65c8 · outbound

This paper cites an unresolved cited work.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:20:48.782592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 97e64e60-6e2f-44e3-9765-c5aa28f60d0a · outbound

This paper cites Envisioning better benchmarks for machine learning pde solvers.Nature Machine Intelligence, 7(1):2–3, jan 2025.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Envisioning better benchmarks for machine learning pde solvers.Nature Machine Intelligence, 7(1):2–3, jan 2025

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.773679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ef21f2a1-ae2c-474d-bd21-3060c87b8a11 · outbound

This paper cites Leveque.Numerical Methods for Conservation Laws.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Leveque.Numerical Methods for Conservation Laws

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.763407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 42a6e847-0ea4-4113-b4bd-fb5b49c26de4 · outbound

This paper cites Comparative performance analysis of numerical discretization methods for electro- chemical model of lithium-ion batteries.Journal of Power Sources, 650:237365, 09 2025.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Comparative performance analysis of numerical discretization methods for electro- chemical model of lithium-ion batteries.Journal of Power Sources, 650:237365, 09 2025

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.754288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 431a9786-218e-4c17-a25b-8ecd4c0d46ea · outbound

This paper cites Meerschaert and Charles Tadjeran.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Meerschaert and Charles Tadjeran

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.745037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 95b98b8f-b537-4bfe-b1e1-f691dce6a542 · outbound

This paper cites Alikhanov.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Alikhanov

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.736579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 22c9b9c8-80c6-44c1-ad52-eadf9a151b73 · outbound

This paper cites an unresolved cited work.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:20:48.728242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bd800e82-f46f-402a-9d55-e85c2d24aa45 · outbound

This paper cites Moghaddam and J.A.T.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Moghaddam and J.A.T

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.719943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 34c9275f-60e3-4b1d-abd1-1dd789377b9e · outbound

This paper cites Vadivel, Nallappan Gunasekaran, Haitao Zhu, Jinde Cao, and Xiaodi Li.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Vadivel, Nallappan Gunasekaran, Haitao Zhu, Jinde Cao, and Xiaodi Li

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.710197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f315c101-39d8-4f7b-a21f-9709ee390b52 · outbound

This paper cites Godunov and I.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Godunov and I

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.702224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6c78e375-d01f-4bff-933d-a0f5b79adf3d · outbound

This paper cites Mfem: A modular finite element methods library.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Mfem: A modular finite element methods library

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.693925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 50fc44ad-1222-4e29-813f-adbe19375eed · outbound

This paper cites High-order finite element methods for time-fractional partial differential equations.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs High-order finite element methods for time-fractional partial differential equations

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.685403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e34ccfaf-faee-4d6d-8a4b-8d6aeb2bcb0b · outbound

This paper cites Gunzburger, Clayton G.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Gunzburger, Clayton G

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.676708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1e0e5f67-3757-4b91-a3c6-f05c4adba41a · outbound

This paper cites The local discontinuous galerkin finite element methods for caputo-type partial differential equations: Mathematical analysis.Applied Numerical Mathematics, 150:587–606, 2020.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs The local discontinuous galerkin finite element methods for caputo-type partial differential equations: Mathematical analysis.Applied Numerical Mathematics, 150:587–606, 2020

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.667205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2269faa4-1af5-4caf-ab0a-91b20b64a54f · outbound

This paper cites an unresolved cited work.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:20:48.657846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c126c118-fa29-4c31-b366-73c0c9a73ffc · outbound

This paper cites Finite volume methods.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Finite volume methods

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.649438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 25054e08-35df-49f6-985e-131eeb46aab0 · outbound

This paper cites Spectral solutions for the time-fractional heat differential equation through a novel unified sequence of chebyshev polynomials.AIMS MATHEMATICS, 9(1):2137–2166, 2024.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Spectral solutions for the time-fractional heat differential equation through a novel unified sequence of chebyshev polynomials.AIMS MATHEMATICS, 9(1):2137–2166, 2024

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.640088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d2d859ec-9083-4ed8-ac63-482e2a0c0dee · outbound

This paper cites an unresolved cited work.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:20:48.629955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fb678659-9930-4bd7-8b79-8ea532e3509f · outbound

This paper cites Spectral methods in fluid dynamics (c.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Spectral methods in fluid dynamics (c

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.620253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 469e2b9b-d6e2-4968-bb04-d65a2b957a08 · outbound

This paper cites Hauck, and Stanley Osher.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Hauck, and Stanley Osher

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.611621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a8c2379c-c944-40ae-8806-e43da1f7987c · outbound

This paper cites Highly accurate protein structure prediction with alphafold.Nature, pages 1–11, 2021.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Highly accurate protein structure prediction with alphafold.Nature, pages 1–11, 2021

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.602219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 26f05e48-e213-4257-89a9-9e1cba6c47da · outbound

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

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Deep learning for reduced order modelling and efficient temporal evolution of fluid simulations

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.593000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 99ce7b4d-b21f-4235-978a-09d00c5ebf8a · outbound

This paper cites Read, Jacob A.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Read, Jacob A

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.583399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d380f391-075f-4010-87be-23f916ec4494 · outbound

This paper cites Integrating scientific knowledge with machine learning for engineering and environmental systems.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Integrating scientific knowledge with machine learning for engineering and environmental systems

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.574149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c3bc29c0-5223-4b26-969a-ed0aead4fe3d · outbound

This paper cites Meaningless comparisons lead to false optimism in medical machine learning.Plos One, 12(9), 2017.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Meaningless comparisons lead to false optimism in medical machine learning.Plos One, 12(9), 2017

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.564905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 227592f7-ff5c-4750-97fc-2d0d479e4ed1 · outbound

This paper cites Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.553986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ba11344e-4c21-47de-838d-697c91e2a647 · outbound

This paper cites Wujek and Patrick Hall.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Wujek and Patrick Hall

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.544328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9d8fb599-7be1-4679-9d96-5abb0df62caa · outbound

This paper cites Raissi, P.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Raissi, P

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T10:20:48.303628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c681537-843e-4c7b-9ab7-3949af8dd426 · outbound

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

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Encoding physics to learn reaction–diffusion processes.Nature Machine Intelligence, 5(7):765–779, jul 2023

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.529684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 78243319-b2c8-44d4-92b0-e62a24ad38a8 · outbound

This paper cites Karniadakis.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Karniadakis

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T10:20:48.309640Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93c608fc-83f4-4926-a5f9-026f71ba8ee9 · outbound

This paper cites Enhancing convergence speed with feature enforcing physics-informed neural networks using boundary conditions as prior knowledge.Scientific Reports, 14(1):23836, oct 11 2024.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Enhancing convergence speed with feature enforcing physics-informed neural networks using boundary conditions as prior knowledge.Scientific Reports, 14(1):23836, oct 11 2024

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.515027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5ce3de4e-95a1-4af6-a66d-76525edb85f9 · outbound

This paper cites McClenny and Ulisses M.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs McClenny and Ulisses M

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T10:20:48.316388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36de2dfd-2585-4fed-9619-75f374ec919f · outbound

This paper cites and Jia Zhao.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs and Jia Zhao

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.499848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b563cda9-6066-4f03-9a38-20f0411f69ec · outbound

This paper cites E-pinn: A fast physics-informed neural network based on explicit time-domain method for dynamic response prediction of nonlinear structures.Engineering Structures, 321:118900, 2024.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs E-pinn: A fast physics-informed neural network based on explicit time-domain method for dynamic response prediction of nonlinear structures.Engineering Structures, 321:118900, 2024

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.490570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 66616c8b-7df0-4246-a729-cb9b89988c58 · outbound

This paper cites Pi-lstm: Physics-informed long short-term memory network for structural response modeling.Engineering Structures, 292:116500, 2023.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Pi-lstm: Physics-informed long short-term memory network for structural response modeling.Engineering Structures, 292:116500, 2023

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.478676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 13795155-362d-4ab2-9216-097cc3c3db96 · outbound

This paper cites Physics-informed multi-lstm networks for metamodeling of nonlinear structures.Computer Methods in Applied Mechanics and Engineering, 369:113226, 2020.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Physics-informed multi-lstm networks for metamodeling of nonlinear structures.Computer Methods in Applied Mechanics and Engineering, 369:113226, 2020

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.468784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 10fbd5d4-4b5c-4251-9c15-dd58bf69ad16 · outbound

This paper cites Ppinn: Parareal physics-informed neural network for time-dependent pdes.Computer Methods in Applied Mechanics and Engineering, 370:113250, 2020.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Ppinn: Parareal physics-informed neural network for time-dependent pdes.Computer Methods in Applied Mechanics and Engineering, 370:113250, 2020

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T10:20:48.334253Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c92d90b8-1d7e-4af8-8885-915d9b1ae489 · outbound

This paper cites and Em Karniadakis, George.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs and Em Karniadakis, George

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.453223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dcce97a0-acec-4035-b8c5-acb71f6de279 · outbound

This paper cites Jagtap, Kenji Kawaguchi, and George Em Karniadakis.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Jagtap, Kenji Kawaguchi, and George Em Karniadakis

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.442947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0503b4d5-812c-4646-b686-ac31f7956e74 · outbound

This paper cites Deep learning method based on physics informed neural network with resnet block for solving fluid flow problems.Water, (4), 2021.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Deep learning method based on physics informed neural network with resnet block for solving fluid flow problems.Water, (4), 2021

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.433555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4270b525-3a80-48b4-b084-2e11e9377bf9 · outbound

This paper cites Understanding and mitigating gradient pathologies in physics-informed neural networks.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Understanding and mitigating gradient pathologies in physics-informed neural networks

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T10:20:48.347061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09c51b06-5b95-46b5-899a-05e71034516a · outbound

This paper cites Multi-scale deep neural network (mscalednn) for solving poisson- boltzmann equation in complex domains.Communications in Computational Physics, 28(5):1970–2001, 2020.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Multi-scale deep neural network (mscalednn) for solving poisson- boltzmann equation in complex domains.Communications in Computational Physics, 28(5):1970–2001, 2020

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.423750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6b752166-0781-4672-bedc-d108c529cf50 · outbound

This paper cites Automatic differentiation in machine learning: A survey.Journal of Machine Learning Research, 18:1–43, 04 2018.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Automatic differentiation in machine learning: A survey.Journal of Machine Learning Research, 18:1–43, 04 2018

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.414318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1af2b854-cdd4-4775-8022-c5c6e81c7f0e · outbound

This paper cites Deepxde: A deep learning library for solving differential equations.SIAM Review, 63(1):208–228, 2021.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Deepxde: A deep learning library for solving differential equations.SIAM Review, 63(1):208–228, 2021

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T10:20:48.357058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de466887-82d5-4bdb-a58b-a83fdca6129d · outbound

This paper cites Compatibility conditions for systems of iterative functional equations with non-trivial contact sets.Results in Mathematics, 76(2):68, mar 17 2021.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Compatibility conditions for systems of iterative functional equations with non-trivial contact sets.Results in Mathematics, 76(2):68, mar 17 2021

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:48.399221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers