Fetching the paper…
Reading the bibliography…
Aortic dissection progresses via delamination of the medial layer of the wall.
Factors in the propagation of aortic dissections in canine thoracic aortas
van Baardwijk, C. and M. R. Roach · 1987
Earlier work this paper cites.
The strength of the aortic media and its role in the propagation of aortic dissection
Carson, M. W. and M. R. Roach · 1990
Earlier work this paper cites.
The composition and mechanical properties of abdominal aortic aneurysms
He, C. M. and M. R. Roach · 1994
Earlier work this paper cites.
Variations in strength of the porcine aorta as a function of location
Roach, M. R. and S. Song · 1994
Earlier work this paper cites.
Artificial neural networks for solving ordinary and partial differential equations
Lagaris, I. E., A. Likas, and D. I. Fotiadis · 1998
Earlier work this paper cites.
The effect of tear depth on the propagation of aortic dissections in isolated porcine thoracic aorta
Tam, A. S., M. C. Sapp, and M. R. Roach · 1998
Earlier work this paper cites.
The role of radial elastic properties in the development of aortic dissections
MacLean, N. F., N. L. Dudek, and M. R. Roach · 1999
Earlier work this paper cites.
A new constitutive framework for arterial wall mechanics and a comparative study of material models
Holzapfel, G. A., T. C. Gasser, and R. W. Ogden · 2000
Earlier work this paper cites.
Age dependency of the biaxial biomechanical behavior of human abdominal aorta
Geest, J. P. V., M. S. Sacks, and D. A. Vorp · 2004
Earlier work this paper cites.
Fluid-structure interaction within a layered aortic arch model
Gao, F., Z. Guo, M. Sakamoto, and T. Matsuzawa · 2006
Earlier work this paper cites.
Hyperelastic modelling of arterial layers with distributed collagen fibre orientations
Gasser, T. C., R. W. Ogden, and G. A. Holzapfel · 2006
Earlier work this paper cites.
Towards an understanding of the mechanics underlying aortic dissection
Rajagopal, K., C. Bridges, and K. Rajagopal · 2007
Earlier work this paper cites.
The three-dimensional micro-and nanostructure of the aortic medial lamellar unit measured using 3d confocal and electron microscopy imaging
O’Connell, M. K., S. Murthy, S. Phan, C. Xu, J. Buchanan, R. Spilker, R. L. Dalman, C. K. Zarins, W. Denk, and C. A. Taylor · 2008
Earlier work this paper cites.
Dissection properties of the human aortic media: an experimental study
Sommer, G., T. C. Gasser, P. Regitnig, M. Auer, and G. A. Holzapfel · 2008
Earlier work this paper cites.
Kernel density estimation via diffusion
Botev, Z. I., J. F. Grotowski, and D. P. Kroese · 2010
Earlier work this paper cites.
Pathogenesis of acute aortic dissection: a finite element stress analysis
Nathan, D. P., C. Xu, J. H. Gorman III, R. M. Fairman, J. E. Bavaria, R. C. Gorman, K. B. Chandran, and B. M. Jackson · 2011
Earlier work this paper cites.
Longitudinal computational fluid dynamics study of aneurysmal dilatation in a chronic debakey type iii aortic dissection
Karmonik, C., S. Partovi, M. Müller-Eschner, J. Bismuth, M. G. Davies, D. J. Shah, M. Loebe, D. Böckler, A. B. Lumsden, and H. von Tengg-Kobligk · 2012
Earlier work this paper cites.
Possible mechanical roles of glycosaminoglycans in thoracic aortic dissection and associations with dysregulated transforming growth factor- β \beta
Humphrey, J. D · 2013
Earlier work this paper cites.
Computational fluid dynamics investigation of chronic aortic dissection hemodynamics versus normal aorta
Karmonik, C., M. Müller-Eschner, S. Partovi, P. Geisbüsch, M.-K. Ganten, J. Bismuth, M. G. Davies, D. Böckler, M. Loebe, A. B. Lumsden, and H. von Tengg-Kobligk · 2013
Earlier work this paper cites.
Biomechanical roles of medial pooling of glycosaminoglycans in thoracic aortic dissection
Roccabianca, S., G. A. Ateshian, and J. D. Humphrey · 2014
Earlier work this paper cites.
Computational modelling suggests good, bad and ugly roles of glycosaminoglycans in arterial wall mechanics and mechanobiology
Roccabianca, S., C. Bellini, and J. D. Humphrey · 2014
Cited alongside, same era.
Quantification of regional differences in aortic stiffness in the aging human
Roccabianca, S., C. Figueroa, G. Tellides, and J. D. Humphrey · 2014
Cited alongside, same era.
The fenics project version 1.5
Alnæs, M., J. Blechta, J. Hake, A. Johansson, B. Kehlet, A. Logg, C. Richardson, J. Ring, M. E. Rognes, and G. N. Wells · 2015
Cited alongside, same era.
Patient-specific finite element analysis of ascending aorta aneurysms
Martin, C., W. Sun, and J. Elefteriades · 2015
Cited alongside, same era.
Mechanical assessment of arterial dissection in health and disease: advancements and challenges
Tong, J., Y. Cheng, and G. A. Holzapfel · 2016
Cited alongside, same era.
Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4d flow mri data using physics-informed neural networks
Kissas, G., Y. Yang, E. Hwuang, W. R. Witschey, J. A. Detre, and P. Perdikaris · 2020
Later among the works it cites.
Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders
Lee, K. and K. T. Carlberg · 2020
Later among the works it cites.
Fourier neural operator for parametric partial differential equations
Li, Z., N. Kovachki, K. Azizzadenesheli, B. Liu, K. Bhattacharya, A. Stuart, and A. Anandkumar · 2020
Later among the works it cites.
Operator learning for predicting multiscale bubble growth dynamics
Lin, C., Z. Li, L. Lu, S. Cai, M. Maxey, and G. E. Karniadakis · 2020
Later among the works it cites.
Physics-informed neural networks for high-speed flows
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Rausch, M. K., G. E. Karniadakis, and J. D. Humphrey · 2017
Cited alongside, same era.
Particle-based computational modelling of arterial disease
Ahmadzadeh, H., M. K. Rausch, and J. D. Humphrey · 2018
Cited alongside, same era.
Computational fluid dynamic accuracy in mimicking changes in blood hemodynamics in patients with acute type iiib aortic dissection treated with tevar
Polanczyk, A., A. Piechota-Polanczyk, C. Domenig, J. Nanobachvili, I. Huk, and C. Neumayer · 2018
Cited alongside, same era.
Modeling lamellar disruption within the aortic wall using a particle-based approach
Ahmadzadeh, H., M. K. Rausch, and J. D. Humphrey · 2019
Cited alongside, same era.
Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences
Alber, M., A. Buganza Tepole, W. R. Cannon, S. De, S. Dura-Bernal, K. Garikipati, G. E. Karniadakis, W. W. Lytton, P. Perdikaris, L. Petzold et al · 2019
Cited alongside, same era.
Computational modeling of progressive damage and rupture in fibrous biological tissues: application to aortic dissection
Gültekin, O., S. P. Hager, H. Dal, and G. A. Holzapfel · 2019
Cited alongside, same era.
Patient-specific computational hemodynamic analysis for interrupted aortic arch in an adult: implications for aortic dissection initiation
Peng, L., Y. Qiu, Z. Yang, D. Yuan, C. Dai, D. Li, Y. Jiang, and T. Zheng · 2019
Cited alongside, same era.
Mao, Z., A. D. Jagtap, and G. E. Karniadakis · 2020
Later among the works it cites.
Lift & learn: Physics-informed machine learning for large-scale nonlinear dynamical systems
Qian, E., B. Kramer, B. Peherstorfer, and K. Willcox · 2020
Later among the works it cites.
Physics-informed neural networks for cardiac activation mapping
Sahli Costabal, F., Y. Yang, P. Perdikaris, D. E. Hurtado, and E. Kuhl · 2020
Later among the works it cites.
Avalanches and power law behavior in aortic dissection propagation
Yu, X., B. Suki, and Y. Zhang · 2020
Later among the works it cites.
Physics-informed neural networks for nonhomogeneous material identification in elasticity imaging
Zhang, E., M. Yin, and G. E. Karniadakis · 2020
Later among the works it cites.
Differential propensity of dissection along the aorta
Ban, E., C. Cavinato, and J. D. Humphrey · 2021
Closest in time.
Physics-informed neural networks (PINNs) for fluid mechanics: A review
Cai, S., Z. Mao, Z. Wang, M. Yin, and G. E. Karniadakis · 2021
Closest in time.
Evolving structure-function relations during aortic maturation and aging revealed by multiphoton microscopy
Cavinato, C., S.-I. Murtada, A. Rojas, and J. D. Humphrey · 2021
Closest in time.
A physics-informed variational deeponet for predicting the crack path in brittle materials
Goswami, S., M. Yin, Y. Yu, and G. E. Karniadakis · 2021
Closest in time.
NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible navier-stokes equations
Jin, X., S. Cai, H. Li, and G. E. Karniadakis · 2021
Closest in time.
Physics-informed machine learning
Karniadakis, G. E., I. G. Kevrekidis, L. Lu, P. Perdikaris, W. Sifan, and L. Yang · 2021
Closest in time.
Learning nonlinear operators via deeponet based on the universal approximation theorem of operators
Lu, L., P. Jin, G. Pang, Z. Zhang, and G. E. Karniadakis · 2021
Closest in time.
Wang, S., H. Wang, and P. Perdikaris · 2021
Closest in time.
Parameter identification for phase-field modeling of fracture: a bayesian approach with sampling-free update
Wu, T., B. Rosić, L. De Lorenzis, and H. G. Matthies · 2021
Closest in time.
Non-invasive inference of thrombus material properties with physics-informed neural networks
Yin, M., X. Zheng, J. D. Humphrey, and G. E. Karniadakis · 2021
Closest in time.