Non-invasive inference of thrombus material properties with physics-informed neural networks
出版年份 2020 全文链接
标题
Non-invasive inference of thrombus material properties with physics-informed neural networks
作者
关键词
Viscoelastic porous material, Physics-informed neural networks, Inverse problem, Phase field model, Computational fluids dynamics
出版物
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
Volume 375, Issue -, Pages 113603
出版商
Elsevier BV
发表日期
2020-12-23
DOI
10.1016/j.cma.2020.113603
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Learning the Physics of Pattern Formation from Images
- (2020) Hongbo Zhao et al. PHYSICAL REVIEW LETTERS
- Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
- (2020) Maziar Raissi et al. SCIENCE
- Physics-informed neural networks for inverse problems in nano-optics and metamaterials
- (2020) Yuyao Chen et al. OPTICS EXPRESS
- Extraction of mechanical properties of materials through deep learning from instrumented indentation
- (2020) Lu Lu et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- A three-dimensional phase-field model for multiscale modeling of thrombus biomechanics in blood vessels
- (2020) Xiaoning Zheng et al. PLoS Computational Biology
- Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems
- (2020) Ameya D. Jagtap et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems
- (2019) Dongkun Zhang et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Variational system identification of the partial differential equations governing the physics of pattern-formation: Inference under varying fidelity and noise
- (2019) Z. Wang et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks
- (2019) Georgios Kissas et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Physics-informed neural networks for high-speed flows
- (2019) Zhiping Mao et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems
- (2019) Xuhui Meng et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Application of optimal transport and the quadratic Wasserstein metric to full-waveform inversion
- (2018) Yunan Yang et al. GEOPHYSICS
- Using Deep Neural Networks for Inverse Problems in Imaging: Beyond Analytical Methods
- (2018) Alice Lucas et al. IEEE SIGNAL PROCESSING MAGAZINE
- Deep D-bar: Real time Electrical Impedance Tomography Imaging with Deep Neural Networks
- (2018) S. J. Hamilton et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- (2018) M. Raissi et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Deep Convolutional Neural Network for Inverse Problems in Imaging
- (2017) Kyong Hwan Jin et al. IEEE TRANSACTIONS ON IMAGE PROCESSING
- A Computational Model of the Biochemomechanics of an Evolving Occlusive Thrombus
- (2017) Manuel K. Rausch et al. JOURNAL OF ELASTICITY
- Model predictions of deformation, embolization and permeability of partially obstructive blood clots under variable shear flow
- (2017) Shixin Xu et al. Journal of the Royal Society Interface
- A General Shear-Dependent Model for Thrombus Formation
- (2017) Alireza Yazdani et al. PLoS Computational Biology
- Data-driven discovery of partial differential equations
- (2017) Samuel H. Rudy et al. Science Advances
- State and parameter estimation in 1-D hyperbolic PDEs based on an adjoint method
- (2016) Van Tri Nguyen et al. AUTOMATICA
- Stochastic shale permeability matching: Three-dimensional characterization and modeling
- (2016) Pejman Tahmasebi et al. INTERNATIONAL JOURNAL OF COAL GEOLOGY
- A microstructurally inspired damage model for early venous thrombus
- (2016) Manuel K. Rausch et al. Journal of the Mechanical Behavior of Biomedical Materials
- Deep vein thrombosis and pulmonary embolism
- (2016) Marcello Di Nisio et al. LANCET
- Discovering governing equations from data by sparse identification of nonlinear dynamical systems
- (2016) Steven L. Brunton et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Multicomponent model of deformation and detachment of a biofilm under fluid flow
- (2015) G. Tierra et al. Journal of the Royal Society Interface
- The Hydraulic Permeability of Blood Clots as a Function of Fibrin and Platelet Density
- (2013) A.R. Wufsus et al. BIOPHYSICAL JOURNAL
- Parameter Estimation of Partial Differential Equation Models
- (2013) Xiaolei Xun et al. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
- Goal-Oriented Inference: Approach, Linear Theory, and Application to Advection Diffusion
- (2013) Chad Lieberman et al. SIAM REVIEW
- Parameter Estimation for Differential Equation Models Using a Framework of Measurement Error in Regression Models
- (2009) Hua Liang et al. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
- A multiscale model of thrombus development
- (2007) Z. Xu et al. Journal of the Royal Society Interface
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now