Data-driven Tissue Mechanics with Polyconvex Neural Ordinary Differential Equations [0.03%]
基于多凸神经常微分方程的:data驱动组织力学
Vahidullah Tac,Francisco Sahli Costabal,Adrian B Tepole
Vahidullah Tac
Data-driven methods are becoming an essential part of computational mechanics due to their advantages over traditional material modeling. Deep neural networks are able to learn complex material response without the constraints of closed-for...
Erica L Schwarz,Martin R Pfaller,Jason M Szafron et al.
Erica L Schwarz et al.
We implement full, three-dimensional constrained mixture theory for vascular growth and remodeling into a finite element fluid-structure interaction (FSI) solver. The resulting "fluid-solid-growth" (FSG) solver allows long term, patient-spe...
Data-driven Uncertainty Quantification in Computational Human Head Models [0.03%]
基于数据的人体头部计算模型不确定性分析
Kshitiz Upadhyay,Dimitris G Giovanis,Ahmed Alshareef et al.
Kshitiz Upadhyay et al.
Computational models of the human head are promising tools for estimating the impact-induced response of the brain, and thus play an important role in the prediction of traumatic brain injury. The basic constituents of these models (i.e., m...
Branched Latent Neural Maps [0.03%]
分支潜变量神经地图
Matteo Salvador,Alison Lesley Marsden
Matteo Salvador
We introduce Branched Latent Neural Maps (BLNMs) to learn finite dimensional input-output maps encoding complex physical processes. A BLNM is defined by a simple and compact feedforward partially-connected neural network that structurally d...
Optimizing combination therapy in a murine model of HER2+ breast cancer [0.03%]
HER2阳性乳腺癌小鼠模型的联合治疗优化
Ernesto A B F Lima,Reid A F Wyde,Anna G Sorace et al.
Ernesto A B F Lima et al.
Human epidermal growth factor receptor 2 positive (HER2+) breast cancer is frequently treated with drugs that target the HER2 receptor, such as trastuzumab, in combination with chemotherapy, such as doxorubicin. However, an open problem in ...
A stabilized linear finite element method for anisotropic poroelastodynamics with application to cardiac perfusion [0.03%]
各向异性渗透弹性动力学的线性有限元稳定化方法及其在心脏灌注中的应用
Namshad Thekkethil,Simone Rossi,Hao Gao et al.
Namshad Thekkethil et al.
We propose a variational multiscale method stabilization of a linear finite element method for nonlinear poroelasticity. Our approach is suitable for the implicit time integration of poroelastic formulations in which the solid skeleton is a...
Data-driven anisotropic finite viscoelasticity using neural ordinary differential equations [0.03%]
基于神经常微分方程的数据驱动各向异性有限粘弹性模型
Vahidullah Taç,Manuel Rausch,Francisco Sahli Costabal et al.
Vahidullah Taç et al.
We develop a fully data-driven model of anisotropic finite viscoelasticity using neural ordinary differential equations as building blocks. We replace the Helmholtz free energy function and the dissipation potential with data-driven functio...
Interfacing finite elements with deep neural operators for fast multiscale modeling of mechanics problems [0.03%]
基于有限元与深度神经网络接口的力学问题多尺度快速建模方法
Minglang Yin,Enrui Zhang,Yue Yu et al.
Minglang Yin et al.
Multiscale modeling is an effective approach for investigating multiphysics systems with largely disparate size features, where models with different resolutions or heterogeneous descriptions are coupled together for predicting the system's...
Strain energy density as a Gaussian process and its utilization in stochastic finite element analysis: application to planar soft tissues [0.03%]
应变能密度作为高斯过程及其在随机有限元分析中的应用：平面软组织的应用
Ankush Aggarwal,Bjørn Sand Jensen,Sanjay Pant et al.
Ankush Aggarwal et al.
Data-based approaches are promising alternatives to the traditional analytical constitutive models for solid mechanics. Herein, we propose a Gaussian process (GP) based constitutive modeling framework, specifically focusing on planar, hyper...