FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4220662102> ?p ?o ?g. }

• W4220662102 endingPage "114871" @default.
• W4220662102 startingPage "114871" @default.
• W4220662102 abstract "High-fidelity cardiac models using attribute-rich finite element based models have been developed to a very mature stage. However, such finite-element based approaches remain time consuming, which have limited their clinical use. There remains a need for alternative methods for novel cardiac simulation methods of capable of high fidelity simulations in clinically relevant time frames. Surrogate models are one approach, which traditionally use a data-driven approach for training, requiring the generation of a sufficiently large number of simulation results as the training dataset. Alternatively, a physics-informed neural network can be trained by minimizing the PDE residuals or energy potentials. However, this approach does not provide for a general method to easily using existing finite element models. To address these challenges, we developed a hybrid approach that seamlessly bridged a neural network surrogate model with a differentiable finite element domain representation (NNFE). Given its importance in cardiac simulations, we applied this approach to simulations of the hyperelastic mechanical behavior of ventricular myocardium from recent 3D kinematic constitutive model (J Mech Behav Biomed Mater, 2020 doi: 10.1016/j.jmbbm.2019.103508). We utilized cuboidal domain and conducted numerical studies of individual myocardium specimens discretized by a finite element mesh and assigned with experimentally obtained myofiber architectures. Both parameterized Dirichlet and Neumann boundary conditions were studied. We developed a second-order Newton optimization method, instead of using stochastic gradient descent method, to train the neural network efficiently. The resulting trained neural network surrogate model demonstrated excellent agreement with the corresponding 'ground truth' finite element solutions over the entire physiological deformation range. More importantly, the NNFE approach provided a significantly decreased computational time for a range of finite element mesh sizes for online predictions. For example, as the finite element mesh sized increased from 2744 to 175615 elements the NNFE computational time increased from 0.1108 s to 0.1393 s, while the 'ground truth' FE model increased from 4.541 s to 719.9 s. These results suggests that NNFE run times can be significantly reduced compared with the traditional large-deformation based finite element solution methods. The trade off is to train the NNFE off-line within a range of anticipated physiological responses. However, training time would only have to be performed once before any number of application uses. Moreover, since the NNFE is an analytical function its computational performance will be amplified when the corresponding problem becomes more complex." @default.
• W4220662102 created "2022-04-03" @default.
• W4220662102 creator A5008274019 @default.
• W4220662102 creator A5035229069 @default.
• W4220662102 creator A5057816481 @default.
• W4220662102 creator A5060370519 @default.
• W4220662102 date "2022-05-01" @default.
• W4220662102 modified "2023-10-14" @default.
• W4220662102 title "Simulation of the 3D hyperelastic behavior of ventricular myocardium using a finite-element based neural-network approach" @default.
• W4220662102 cites W1966450732 @default.
• W4220662102 cites W1966543040 @default.
• W4220662102 cites W1967134278 @default.
• W4220662102 cites W1979396528 @default.
• W4220662102 cites W1982029046 @default.
• W4220662102 cites W1983043061 @default.
• W4220662102 cites W1983859428 @default.
• W4220662102 cites W1985926778 @default.
• W4220662102 cites W1986005020 @default.
• W4220662102 cites W2005163420 @default.
• W4220662102 cites W2008034786 @default.
• W4220662102 cites W2018159038 @default.
• W4220662102 cites W2026398067 @default.
• W4220662102 cites W2028446666 @default.
• W4220662102 cites W2043938150 @default.
• W4220662102 cites W2060528584 @default.
• W4220662102 cites W2063467924 @default.
• W4220662102 cites W2068998039 @default.
• W4220662102 cites W2070929467 @default.
• W4220662102 cites W2079884250 @default.
• W4220662102 cites W2089194947 @default.
• W4220662102 cites W2102339840 @default.
• W4220662102 cites W2114898591 @default.
• W4220662102 cites W2132135840 @default.
• W4220662102 cites W2132333652 @default.
• W4220662102 cites W2135781034 @default.
• W4220662102 cites W2151635674 @default.
• W4220662102 cites W2152896489 @default.
• W4220662102 cites W2164489671 @default.
• W4220662102 cites W2170745076 @default.
• W4220662102 cites W2194775991 @default.
• W4220662102 cites W2218013993 @default.
• W4220662102 cites W2291401466 @default.
• W4220662102 cites W2523776987 @default.
• W4220662102 cites W2619519104 @default.
• W4220662102 cites W2752312579 @default.
• W4220662102 cites W2760972773 @default.
• W4220662102 cites W2771179040 @default.
• W4220662102 cites W2784733489 @default.
• W4220662102 cites W2786232134 @default.
• W4220662102 cites W2883885623 @default.
• W4220662102 cites W2893674051 @default.
• W4220662102 cites W2899283552 @default.
• W4220662102 cites W2908541468 @default.
• W4220662102 cites W2948230027 @default.
• W4220662102 cites W2952645301 @default.
• W4220662102 cites W2982123645 @default.
• W4220662102 cites W2987858705 @default.
• W4220662102 cites W3010152544 @default.
• W4220662102 cites W3041133507 @default.
• W4220662102 cites W3046060246 @default.
• W4220662102 cites W3111914315 @default.
• W4220662102 cites W3175648064 @default.
• W4220662102 cites W3186109400 @default.
• W4220662102 cites W844253406 @default.
• W4220662102 doi "https://doi.org/10.1016/j.cma.2022.114871" @default.
• W4220662102 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35422534" @default.
• W4220662102 hasPublicationYear "2022" @default.
• W4220662102 type Work @default.
• W4220662102 citedByCount "8" @default.
• W4220662102 countsByYear W42206621022022 @default.
• W4220662102 countsByYear W42206621022023 @default.
• W4220662102 crossrefType "journal-article" @default.
• W4220662102 hasAuthorship W4220662102A5008274019 @default.
• W4220662102 hasAuthorship W4220662102A5035229069 @default.
• W4220662102 hasAuthorship W4220662102A5057816481 @default.
• W4220662102 hasAuthorship W4220662102A5060370519 @default.
• W4220662102 hasBestOaLocation W42206621021 @default.
• W4220662102 hasConcept C11413529 @default.
• W4220662102 hasConcept C119857082 @default.
• W4220662102 hasConcept C127413603 @default.
• W4220662102 hasConcept C131675550 @default.
• W4220662102 hasConcept C134306372 @default.
• W4220662102 hasConcept C135628077 @default.
• W4220662102 hasConcept C147370603 @default.
• W4220662102 hasConcept C154945302 @default.
• W4220662102 hasConcept C165464430 @default.
• W4220662102 hasConcept C28826006 @default.
• W4220662102 hasConcept C33923547 @default.
• W4220662102 hasConcept C41008148 @default.
• W4220662102 hasConcept C50644808 @default.
• W4220662102 hasConcept C66938386 @default.
• W4220662102 hasConcept C73000952 @default.
• W4220662102 hasConceptScore W4220662102C11413529 @default.
• W4220662102 hasConceptScore W4220662102C119857082 @default.
• W4220662102 hasConceptScore W4220662102C127413603 @default.
• W4220662102 hasConceptScore W4220662102C131675550 @default.
• W4220662102 hasConceptScore W4220662102C134306372 @default.
• W4220662102 hasConceptScore W4220662102C135628077 @default.

• next