mlsanityData to 7 Oct 2026

Simulating progressive intramural damage leading to aortic dissection using an operator-regression neural network

arXiv 2108.11985 ↗First posted 25 Aug 2021cs.CE cs.LGcited by 35

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Abstract (arXiv)

Aortic dissection progresses via delamination of the medial layer of the wall. Notwithstanding the complexity of this process, insight has been gleaned by studying in vitro and in silico the progression of dissection driven by quasi-static pressurization of the intramural space by fluid injection, which demonstrates that the differential propensity of dissection can be affected by spatial distributions of structurally significant interlamellar struts that connect adjacent elastic lamellae. In particular, diverse histological microstructures may lead to differential mechanical behavior during dissection, including the pressure--volume relationship of the injected fluid and the displacement field between adjacent lamellae. In this study, we develop a data-driven surrogate model for the delamination process for differential strut distributions using DeepONet, a new operator--regression neural network. The surrogate model is trained to predict the pressure--volume curve of the injected fluid and the damage progression field of the wall given a spatial distribution of struts, with in silico data generated with a phase-field finite element model. The results show that DeepONet can provide accurate predictions for diverse strut distributions, indicating that this composite branch-trunk neural network can effectively extract the underlying functional relationship between distinctive microstructures and their mechanical properties. More broadly, DeepONet can facilitate surrogate model-based analyses to quantify biological variability, improve inverse design, and predict mechanical properties based on multi-modality experimental data.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). Builds on 13 of its 79 references that are in the map. First posted on arXiv 25 Aug 2021.

Builds on

13 of 79 references.

References · 13

  1. 2020Fourier Neural Operator for Parametric Partial Differential Equationscited by 2,806
  2. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,320
  3. 2019DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operatorscited by 1,965
  4. 2016Neural Architecture Search with Reinforcement Learningcited by 1,489
  5. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,483
  6. 2021Learning the solution operator of parametric partial differential equations with physics-informed DeepOnetscited by 537
  7. 2021Physics-informed neural networks (PINNs) for fluid mechanics: A reviewcited by 470
  8. 2019Machine Learning for Fluid Mechanicscited by 380
  9. 2019Transfer learning enhanced physics informed neural network for phase-field modeling of fracturecited by 116
  10. 2021A physics-informed variational DeepONet for predicting the crack path in brittle materialscited by 98
  11. 2019Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systemscited by 96
  12. 2019Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networkscited by 76
1 more
  1. 2020Physics-Informed Neural Networks for Nonhomogeneous Material Identification in Elasticity Imagingcited by 40

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 11; 2023: 12; 2024: 4; 2025: 4; 2026: 4.

35 papers.

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  1. 2022MIONet: Learning multiple-input operators via tensor productcited by 180
  2. 2022Physics-Informed Deep Neural Operator Networkscited by 110
  3. 2022Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transportcited by 92
  4. 2022Learning Deep Implicit Fourier Neural Operators (IFNOs) with Applications to Heterogeneous Material Modelingcited by 65
  5. 2022Blending Neural Operators and Relaxation Methods in PDE Numerical Solverscited by 53
  6. 2022Interfacing Finite Elements with Deep Neural Operators for Fast Multiscale Modeling of Mechanics Problemscited by 47
  7. 2023Domain Agnostic Fourier Neural Operatorscited by 36
  8. 2022SVD Perspectives for Augmenting DeepONet Flexibility and Interpretabilitycited by 35
  9. 2022INO: Invariant Neural Operators for Learning Complex Physical Systems with Momentum Conservationcited by 28
  10. 2023Rethinking materials simulations: Blending direct numerical simulations with neural operatorscited by 25
  11. 2023Deep Learning in Deterministic Computational Mechanicscited by 21
  12. 2022A Physics-Guided Neural Operator Learning Approach to Model Biological Tissues from Digital Image Correlation Measurementscited by 19
23 more
  1. 2024Peridynamic Neural Operators: A Data-Driven Nonlocal Constitutive Model for Complex Material Responsescited by 18
  2. 2023Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Reviewcited by 15
  3. 2024A finite element-based physics-informed operator learning framework for spatiotemporal partial differential equations on arbitrary domainscited by 15
  4. 2022Neural operator learning of heterogeneous mechanobiological insults contributing to aortic aneurysmscited by 14
  5. 2025Active operator learning with predictive uncertainty quantification for partial differential equationscited by 12
  6. 2023MetaNO: How to Transfer Your Knowledge on Learning Hidden Physicscited by 10
  7. 2023Interpreting and generalizing deep learning in physics-based problems with functional linear modelscited by 9
  8. 2023Neural Operator: Is data all you need to model the world? An insight into the paradigm of data-driven scientific MLcited by 8
  9. 2023A Generative Modeling Framework for Inferring Families of Biomechanical Constitutive Laws in Data-Sparse Regimescited by 8
  10. 2022G2Phinet: Relating Genotype and Biomechanical Phenotype of Tissues with Deep Learningcited by 6
  11. 2023A hybrid Decoder-DeepONet operator regression framework for unaligned observation datacited by 6
  12. 2023DCEM: A deep complementary energy method for solid mechanicscited by 5
  13. 2024Heterogeneous Peridynamic Neural Operators: Discover Biotissue Constitutive Law and Microstructure From Digital Image Correlation Measurementscited by 4
  14. 2025FD-Bench: A Modular and Fair Benchmark for Data-driven Fluid Simulationcited by 4
  15. 2024Physics and geometry informed neural operator network with application to acoustic scatteringcited by 3
  16. 2025Enhanced Vascular Flow Simulations in Aortic Aneurysm via Physics-Informed Neural Networks and Deep Operator Networkscited by 3
  17. 2025Deep Neural ODE Operator Networks for PDEscited by 3
  18. 2023Mechanical Characterization and Inverse Design of Stochastic Architected Metamaterials Using Neural Operatorscited by 2
  19. 2026Enforcing Dirichlet Boundary Conditions in Operator Learningcited by 1
  20. 2023An Operator Learning Framework for Spatiotemporal Super-resolution of Scientific Simulationscited by 0
  21. 2026Tackling multiphysics problems via finite element-guided physics-informed operator learningcited by 0
  22. 2026Physics-Consistent Neural Networks for Learning Deformation and Director Fields in Microstructured Media with Loss-Based Validation Criteriacited by 0
  23. 2026Beyond Pairwise Graphs in Science: Hypergraph Adaptive Wavelet Operators for Parametric PDEscited by 0