Latent PDE mapping for efficient physics-informed learning across geometries with limited data | Paper Citation Record

Observation 734c4500-8b32-463e-af3d-a8f068731eb5 · outbound

This paper cites Meshless physics-informed deep learning method for three-dimensional solid mechanics.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Meshless physics-informed deep learning method for three-dimensional solid mechanics

Reference 1

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Observation b8cb2180-d33c-4022-8eee-2d8e65f77d20 · outbound

This paper cites Machine learning in fluid dynamics—physics- informed neural networks (pinns) using sparse data: A review.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Machine learning in fluid dynamics—physics- informed neural networks (pinns) using sparse data: A review

Reference 2

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Observation ccf5d842-0bb2-4c35-b718-3b9198becf26 · outbound

This paper cites A simple two-variable model of cardiac excitation.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data A simple two-variable model of cardiac excitation

Reference 3

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Observation b9925994-2df2-4739-8930-1afcee721730 · outbound

This paper cites Uncovering near-wall blood flow from sparse data with physics- informed neural networks.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Uncovering near-wall blood flow from sparse data with physics- informed neural networks

Reference 4

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Observation 60c0ffc1-8056-4983-97ab-42e49e6a7b13 · outbound

This paper cites Physics-informed neural networks for transformed geometries and manifolds.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Physics-informed neural networks for transformed geometries and manifolds

Reference 5

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Observation ac6eeb1f-d1e7-4490-b3f0-682df2ca9258 · outbound

This paper cites Solved in unit domain: Jacobinet for differentiable coordinate-transformed pinns.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Solved in unit domain: Jacobinet for differentiable coordinate-transformed pinns

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T05:32:38.849141Z

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Observation e659b4e0-af52-4b18-ba0f-32ce8e25d364 · outbound

This paper cites Engineering Applications of Artificial Intelligence 127, 107324.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Engineering Applications of Artificial Intelligence 127, 107324

Reference 7

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Observation a19fa855-4585-4f1b-a445-dc0049d36a50 · outbound

This paper cites Scientific machine learning through physics–informed neural networks: Where we are and what’s next.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Scientific machine learning through physics–informed neural networks: Where we are and what’s next

Reference 8

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Observation 0aef1815-a330-4a52-bf17-353c91055961 · outbound

This paper cites Physics-informed graph neural network emulation of soft-tissue mechanics.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Physics-informed graph neural network emulation of soft-tissue mechanics

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T05:32:39.189984Z

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Observation 66fb5f76-9779-4cde-bfa5-c45cbad71428 · outbound

This paper cites Pinning cerebral blood flow: analysis of perfusion mri in infants using physics-informed neural networks.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Pinning cerebral blood flow: analysis of perfusion mri in infants using physics-informed neural networks

Reference 10

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Observation 1cea474a-5432-417e-8db5-f62f87c26aaf · outbound

This paper cites Phygeonet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state pdes on irregular domain.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Phygeonet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state pdes on irregular domain

Reference 11

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Observation 4bbbcf1e-0746-42f2-a340-ae3ce8f9b4a0 · outbound

This paper cites Physics-informed graph neural galerkin networks: A unified framework for solving pde-governed forward and inverse problems.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Physics-informed graph neural galerkin networks: A unified framework for solving pde-governed forward and inverse problems

Reference 12

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Observation 6bb1b64b-597d-4494-8721-2c6a89aec7c8 · outbound

This paper cites Physics-informed deep neural operator networks, in: Machine learning in modeling and simulation: methods and applications.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Physics-informed deep neural operator networks, in: Machine learning in modeling and simulation: methods and applications

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T05:32:39.553420Z

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Observation 58a05aa9-4392-4b0b-bef3-c674a1f1e73d · outbound

This paper cites B-spline curve theory: An overview and applications in real life.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data B-spline curve theory: An overview and applications in real life

Reference 14

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Observation 160eaade-5601-443b-9b4e-7a341f60f06a · outbound

This paper cites Nonlinear Solid Mechanics: A Continuum Approach for Engineering.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Nonlinear Solid Mechanics: A Continuum Approach for Engineering

Reference 15

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Observation 41f22883-741c-4422-9c05-9b9fba0f739f · outbound

This paper cites Principal component analysis: a review and recent developments.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Principal component analysis: a review and recent developments

Reference 16

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Observation 648967e3-a357-4ca9-8f3f-5d207098807b · outbound

This paper cites Physics-informed machine learning.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Physics-informed machine learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T05:32:39.845945Z

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Observation 71d0ea30-2fcf-427f-afcf-fa6e33187e06 · outbound

This paper cites Physics-informed latent neural operator for real-time predictions of time-dependent parametric pdes.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Physics-informed latent neural operator for real-time predictions of time-dependent parametric pdes

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-01T05:32:39.914793Z

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Observation d48c6379-13c2-42b4-b343-f6500863a5a4 · outbound

This paper cites Physics-informed pointnet: A deep learning solver for steady-state incompressible flows and thermal fields on multiple sets of irregular geometries.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Physics-informed pointnet: A deep learning solver for steady-state incompressible flows and thermal fields on multiple sets of irregular geometries

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T05:32:40.026639Z

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Observation 1be8c07a-d232-42e8-bcdb-a65d012b409d · outbound

This paper cites Fourier neural operator with learned deformations for pdes on general geometries.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Fourier neural operator with learned deformations for pdes on general geometries

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T05:32:40.112989Z

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Observation e961cfec-df9a-47f2-ac2a-da99266cc693 · outbound

This paper cites Geometry-informed neural operator for large-scale 3d pdes.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Geometry-informed neural operator for large-scale 3d pdes

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-01T05:32:40.208989Z

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Observation f8ec38f3-209c-4f97-a4cc-34ef612fdae4 · outbound

This paper cites DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T05:32:40.270392Z

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Observation 816fd6a0-1931-44ef-b750-9df7bd1b1b60 · outbound

This paper cites Aerodynamic shape optimization investigations of the common research model wing benchmark.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Aerodynamic shape optimization investigations of the common research model wing benchmark

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-01T05:32:40.357596Z

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Observation 728fd1c7-379e-4e9c-92d8-d02f02c8888b · outbound

This paper cites Right ventricular shape and function: cardiovascular magnetic resonance reference morphology and biventricular risk factor morphometrics in uk biobank.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Right ventricular shape and function: cardiovascular magnetic resonance reference morphology and biventricular risk factor morphometrics in uk biobank

Reference 24

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Observation 548a3b32-19dc-4891-98b7-95f3b96280f5 · outbound

This paper cites A framework for physics-informed deep learning over freeform domains.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data A framework for physics-informed deep learning over freeform domains

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-01T05:32:40.546408Z

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Observation 78f9b1a1-eb20-408a-8841-27407f5e36a5 · outbound

This paper cites A bi-atrial statistical shape model for large-scale in silico studies of human atria: model development and application to ecg simulations.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data A bi-atrial statistical shape model for large-scale in silico studies of human atria: model development and application to ecg simulations

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-01T05:32:40.632352Z

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Observation a6390388-16de-415a-98af-3779bf91214d · outbound

This paper cites Verification of cardiac tissue electrophysiology simulators using an n-version benchmark.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Verification of cardiac tissue electrophysiology simulators using an n-version benchmark

Reference 27

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Observation 375b9f72-8669-4001-801d-e92dfac81995 · outbound

This paper cites Scaling digital twins from the artisanal to the industrial.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Scaling digital twins from the artisanal to the industrial

Reference 28

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Observation 6145141e-2a93-4052-8884-146d49a108f2 · outbound

This paper cites An active strain electromechanical model for cardiac tissue.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data An active strain electromechanical model for cardiac tissue

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-01T05:32:40.923802Z

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Observation 89bfdf81-8702-45f1-bac9-a62888b43375 · outbound

This paper cites Geometry aware physics informed neural network surrogate for solving navier–stokes equation (gapinn).

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Geometry aware physics informed neural network surrogate for solving navier–stokes equation (gapinn)

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-01T05:32:40.984358Z

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Observation b001d1ce-3d0c-4593-8bf4-d3caa3cd39e7 · outbound

This paper cites openCARP.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data openCARP

Reference 31

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Observation d5f02f5e-06a5-4fa5-a280-40e4db104abb · outbound

This paper cites Exploration of continuous variability in collections of 3d shapes.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Exploration of continuous variability in collections of 3d shapes

Reference 32

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Observation 34edecfa-2cd9-4be3-8424-6ff3e12b29ce · outbound

This paper cites Physics-informed graph convolutional neural network for modeling geometry-adaptive steady-state natural convection.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Physics-informed graph convolutional neural network for modeling geometry-adaptive steady-state natural convection

Reference 33

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Observation 7d0e9a04-c978-4d74-b0d2-8ec9a4dce05c · outbound

This paper cites The openCARP simulation environment for cardiac electrophysiology.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data The openCARP simulation environment for cardiac electrophysiology

Reference 34

Resolution: verified exact; doi, observed 2026-08-01T05:33:23.416222Z

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Observation d08c85fb-be77-462b-9009-109951d87cde · outbound

This paper cites Physics-informed neural networks: A deep learning frame- work for solving forward and inverse problems involving nonlinear partial differential equations.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Physics-informed neural networks: A deep learning frame- work for solving forward and inverse problems involving nonlinear partial differential equations

Reference 35

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Observation cf07e4e0-3755-436a-8d1c-fed167d3b68e · outbound

This paper cites Universal solution manifold networks (usm-nets): non-intrusive mesh-free surrogate models for problems in variable domains.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Universal solution manifold networks (usm-nets): non-intrusive mesh-free surrogate models for problems in variable domains

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-01T05:32:41.550982Z

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Observation 1bdb5724-6081-48d9-86a3-076870bb1cd3 · outbound

This paper cites Deeponet-enhanced bayesian inference for biphasic tumor growth modeling.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Deeponet-enhanced bayesian inference for biphasic tumor growth modeling

Reference 37

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This paper cites Physics-informed deep learning for simultaneous surrogate modeling and pde-constrained optimization of an airfoil geometry.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Physics-informed deep learning for simultaneous surrogate modeling and pde-constrained optimization of an airfoil geometry

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-01T05:32:41.720072Z

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Observation d0056b1b-f6af-4ff3-808b-47fd32b9d1aa · outbound

This paper cites Latentpinns: Generative physics-informed neural networks via a latent representation learning.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Latentpinns: Generative physics-informed neural networks via a latent representation learning

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-01T05:32:41.789975Z

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Observation c56a9dc5-d058-448c-831d-3a5059993339 · outbound

This paper cites Artificial intelligence in cardiac electrophysiology: enhancing mapping and ablation precision.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Artificial intelligence in cardiac electrophysiology: enhancing mapping and ablation precision

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-01T05:32:41.899409Z

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Observation 1daa5a34-d33c-405e-8327-9aaf3bde3417 · outbound

This paper cites Exact dirichlet boundary physics-informed neural network epinn for solid mechanics.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Exact dirichlet boundary physics-informed neural network epinn for solid mechanics

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-01T05:32:41.966714Z

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Observation c1034d26-801c-45e1-8d4b-f0de587a1e3f · outbound

This paper cites An Expert's Guide to Training Physics-informed Neural Networks.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data An Expert's Guide to Training Physics-informed Neural Networks

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-01T05:32:42.056258Z

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Observation 2d1d302b-4c39-4c16-99b8-e1b178502839 · outbound

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

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Learning the solution operator of parametric partial differential equations with physics-informed deeponets

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-01T05:32:42.130183Z

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Observation d222bf57-06e1-42de-95b6-a693663fda6a · outbound

This paper cites Individual comparisons by ranking methods.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Individual comparisons by ranking methods

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-01T05:32:42.202025Z

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Observation dea0cfde-ce53-4b92-9541-4d584a3dc9b4 · outbound

This paper cites Physics-informed meshgraphnets (pi- mgns): Neural finite element solvers for non-stationary and nonlinear simulations on arbitrary meshes.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Physics-informed meshgraphnets (pi- mgns): Neural finite element solvers for non-stationary and nonlinear simulations on arbitrary meshes

Reference 45

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Observation dc9243e4-ef00-493d-955e-f44735c5e997 · outbound

This paper cites A scalable framework for learning the geometry-dependent solution operators of partial differential equations.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data A scalable framework for learning the geometry-dependent solution operators of partial differential equations

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-01T05:32:42.431299Z

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Observation 3204a1c1-f354-4172-a42a-97d5c843957b · outbound

This paper cites Physics-informed geometry-aware neural operator.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data Physics-informed geometry-aware neural operator

Reference 47

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