Paper Citation Record · LEDGER

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models

As of 17 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 2 inbound Pith citation observations for arXiv:2505.03382.

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

pith.paper-citation-record.v1
2505.03382 v1

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measured 39 of 39 reference resolution

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Reference resolution

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Outbound references

Observation 531f04dd-d63a-494c-b166-a8101615dd98 · outbound

This paper cites Cardiac function estimation from mri using a heart model and data assimilation: advances and difficulties.Medical image analysis, 10(4):642–656, 2006.

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models Cardiac function estimation from mri using a heart model and data assimilation: advances and difficulties.Medical image analysis, 10(4):642–656, 2006

Reference 1

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Observation 5da55893-3cab-46d0-a814-ef8b07ae8fc3 · outbound

This paper cites A computationally efficient formal optimization of regional myocardial contractility in a sheep with left ventricular aneurysm.

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models A computationally efficient formal optimization of regional myocardial contractility in a sheep with left ventricular aneurysm

Reference 2

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Observation 49ec59d2-9c71-4267-9056-eb10f7a1a326 · outbound

This paper cites Estimating cardiac contraction through high resolution data assim- ilation of a personalized mechanical model.

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models Estimating cardiac contraction through high resolution data assim- ilation of a personalized mechanical model

Reference 3

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Observation f2ac4529-9404-483d-ba84-7fe95ef92d80 · outbound

This paper cites Estimation of passive and active properties in the human heart using 3d tagged mri.Biomechanics and modeling in mechanobiology, 15:1121–1139, 2016.

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models Estimation of passive and active properties in the human heart using 3d tagged mri.Biomechanics and modeling in mechanobiology, 15:1121–1139, 2016

Reference 4

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Observation 6795787d-bdaf-44c8-a1a7-a89ee744d2e3 · outbound

This paper cites Estimation of tissue contractility from cardiac cine-mri using a biomechanical heart model.Biomechanics and modeling in mechanobiology, 11:609–630, 2012.

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models Estimation of tissue contractility from cardiac cine-mri using a biomechanical heart model.Biomechanics and modeling in mechanobiology, 11:609–630, 2012

Reference 5

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Observation 73183366-6003-4682-bf75-808a569ee3fe · outbound

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Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models Unresolved cited work

Reference 6

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Observation 26bc8cb4-874d-4002-b2cd-8968cfcffefa · outbound

This paper cites Estimating cardiac active tension from wall motion—An inverse problem of cardiac biomechanics.

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models Estimating cardiac active tension from wall motion—An inverse problem of cardiac biomechanics

Reference 7

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Observation 3c54d9b0-992a-4168-a681-52913074bc4b · outbound

This paper cites Pozzi, D.

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models Pozzi, D

Reference 8

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Observation 267d9136-856c-4520-83d6-07916f2c7c33 · outbound

This paper cites Physics-informed neural network estimation of material properties in soft tissue nonlinear biomechanical models.Computational Mechanics, pages 1–27, 2024.

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models Physics-informed neural network estimation of material properties in soft tissue nonlinear biomechanical models.Computational Mechanics, pages 1–27, 2024

Reference 9

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Observation aa564ce6-31be-44d2-aaa9-4311d870ff52 · outbound

This paper cites Raissi, P.

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models Raissi, P

Reference 10

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This paper cites A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics.Computer Methods in Applied Mechanics and Engineering, 379:113741, June 2021.

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics.Computer Methods in Applied Mechanics and Engineering, 379:113741, June 2021

Reference 11

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Observation 34e6e3f9-50d3-48bf-ad16-c7e48e5d1983 · outbound

This paper cites Elasticity imaging using physics-informed neural networks: Spatial discovery of elastic modulus and poisson’s ratio.Acta Biomaterialia, 155:400–409, January 2023.

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models Elasticity imaging using physics-informed neural networks: Spatial discovery of elastic modulus and poisson’s ratio.Acta Biomaterialia, 155:400–409, January 2023

Reference 12

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Observation 53511df3-91de-4018-8f07-fe3f0a50ae08 · outbound

This paper cites Rohrhofer, Stefan Posch, Clemens Gößnitzer, and Bernhard C.

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models Rohrhofer, Stefan Posch, Clemens Gößnitzer, and Bernhard C

Reference 13

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This paper cites Holzapfel and Ray W.

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models Holzapfel and Ray W

Reference 14

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Observation a55d5dbc-9355-44ce-814a-67513f2a7ab0 · outbound

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Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models Unresolved cited work

Reference 15

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Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models Unresolved cited work

Reference 16

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This paper cites Active stress vs.

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models Active stress vs

Reference 17

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Observation f22213da-e644-43f0-8c5b-a539fb9bc738 · outbound

This paper cites A biomechanical model of muscle contraction.

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models A biomechanical model of muscle contraction

Reference 18

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Observation 9e7bab5f-d0fb-48a8-a874-604baf836ae0 · outbound

This paper cites A soft- ware benchmark for cardiac elastodynamics.

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models A soft- ware benchmark for cardiac elastodynamics

Reference 19

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This paper cites MIT Press, Cambridge, Mas- sachussets, 2016.

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models MIT Press, Cambridge, Mas- sachussets, 2016

Reference 20

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Observation 15c505be-4bba-4c1b-bf11-577c5da56a88 · outbound

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

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models An Expert's Guide to Training Physics-informed Neural Networks

Reference 21

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This paper cites Anagnostopoulos, Juan Diego Toscano, Nikolaos Stergiopulos, and George Em Karniadakis.

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models Anagnostopoulos, Juan Diego Toscano, Nikolaos Stergiopulos, and George Em Karniadakis

Reference 22

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Observation 8452509a-60d5-48c0-85b8-05a4cc41fc8b · outbound

This paper cites Sukumar and Ankit Srivastava.

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models Sukumar and Ankit Srivastava

Reference 23

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Observation 9cec284d-55e5-4cfa-862a-d7bea5a83347 · outbound

This paper cites On the frequency-bias of coordinate-mlps.

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models On the frequency-bias of coordinate-mlps

Reference 24

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Observation 3055dbb1-ffb7-4a39-b6df-e05fcc9d4002 · outbound

This paper cites Towards Understanding the Spectral Bias of Deep Learning.

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models Towards Understanding the Spectral Bias of Deep Learning

Reference 25

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This paper cites Fourier features let networks learn high frequency functions in low dimensional domains.Advances in neural information processing systems, 33: 7537–7547, 2020.

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models Fourier features let networks learn high frequency functions in low dimensional domains.Advances in neural information processing systems, 33: 7537–7547, 2020

Reference 26

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

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models When and why PINNs fail to train: A neural tangent kernel perspective

Reference 27

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This paper cites Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks.

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks

Reference 28

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Observation ea998c1a-965e-440f-bc38-6c953c874e8d · outbound

This paper cites Augustin, Aurel Neic, Manfred Liebmann, Anton J.

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models Augustin, Aurel Neic, Manfred Liebmann, Anton J

Reference 29

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Observation f019f55a-2a66-4a99-83b0-5b8d58127ee3 · outbound

This paper cites Augustin, Jordi Alastruey, Matthias A.

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models Augustin, Jordi Alastruey, Matthias A

Reference 30

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Observation 6b653267-b851-46dd-9247-7dfe0684c569 · outbound

This paper cites Gsell, Gundolf Haase, Gernot Plank, et al.

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models Gsell, Gundolf Haase, Gernot Plank, et al

Reference 31

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Observation 56e4ab00-57e7-494d-864a-8595f18315df · outbound

This paper cites Warppinn: Cine-mr image registration with physics-informed neural networks.Medical Image Analysis, 89:102925, 2023.

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models Warppinn: Cine-mr image registration with physics-informed neural networks.Medical Image Analysis, 89:102925, 2023

Reference 32

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Observation 7755d5bb-dd86-4ffd-bb1e-9a2bd4248063 · outbound

This paper cites Estimating aggregate cardiomyocyte strain using in vivo diffusion and displacement encoded mri.IEEE transactions on medical imaging, 39(3):656–667, 2019.

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models Estimating aggregate cardiomyocyte strain using in vivo diffusion and displacement encoded mri.IEEE transactions on medical imaging, 39(3):656–667, 2019

Reference 33

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Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models Unresolved cited work

Reference 34

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Observation e5d86202-685e-4157-b5c3-e7574c3a17a7 · outbound

This paper cites Challenges in Training PINNs: A Loss Landscape Perspective.

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models Challenges in Training PINNs: A Loss Landscape Perspective

Reference 35

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Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models Unresolved cited work

Reference 36

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This paper cites doi:https://doi.org/10.1016/j.engappai.2023.107324.

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models doi:https://doi.org/10.1016/j.engappai.2023.107324

Reference 37

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Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models Unresolved cited work

Reference 2001

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This paper cites URL http://dx.doi.org/10.1098/rsta.2009.0091.

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models URL http://dx.doi.org/10.1098/rsta.2009.0091

Reference 2962

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Pith citing papers

Observation 142a147d-d11b-4428-8710-9f6140f736cf · inbound

Cardiac mechanics modeling: recent developments and current challenges cites this paper.

Cardiac mechanics modeling: recent developments and current challenges Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models

Reference 48

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Observation d4f98241-37bc-41e9-845f-1ae0ca916763 · inbound

Personalized 3D Myocardial Infarct Geometry Reconstruction from Cine MRI for Cardiac Digital Twins cites this paper.

Personalized 3D Myocardial Infarct Geometry Reconstruction from Cine MRI for Cardiac Digital Twins Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models

Reference 88

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