Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks

13 May 2019

Papers citing "Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks"

46 / 46 papers shown

Title
Multi-Parameter Molecular MRI Quantification using Physics-Informed Self-Supervised Learning Alex Finkelstein Nikita Vladimirov Moritz Zaiss O. Perlman 35 2 0 10 Nov 2024
Unveiling the optimization process of Physics Informed Neural Networks: How accurate and competitive can PINNs be? Jorge F. Urbán P. Stefanou José A. Pons PINN 45 6 0 07 May 2024
Reconstructing Blood Flow in Data-Poor Regimes: A Vasculature Network Kernel for Gaussian Process Regression S. Z. Ashtiani Mohammad Sarabian K. Laksari H. Babaee 34 2 0 14 Mar 2024
Physics-informed neural networks for blood flow inverse problems Jeremías Garay J. Dunstan S. Uribe F. Sahli Costabal PINN 36 3 0 02 Aug 2023
Physics-Informed Computer Vision: A Review and Perspectives C. Banerjee Kien Nguyen Clinton Fookes G. Karniadakis PINN AI4CE 39 30 0 29 May 2023
Physics-informed machine learning for moving load problems Taniya Kapoor Hongrui Wang Alfredo Núñez R. Dollevoet AI4CE PINN 6 9 0 01 Apr 2023
Viscoelastic Constitutive Artificial Neural Networks (vCANNs) $-$ a framework for data-driven anisotropic nonlinear finite viscoelasticity Kian P. Abdolazizi K. Linka C. Cyron 27 32 0 21 Mar 2023
Learning Reduced-Order Models for Cardiovascular Simulations with Graph Neural Networks Luca Pegolotti Martin R. Pfaller Natalia L. Rubio Ke Ding Rita Brugarolas Brufau Eric F. Darve Alison L. Marsden AI4CE 53 31 0 13 Mar 2023
Temporal Consistency Loss for Physics-Informed Neural Networks Sukirt Thakur M. Raissi H. Mitra A. Ardekani PINN 33 10 0 30 Jan 2023
Physics-informed Information Field Theory for Modeling Physical Systems with Uncertainty Quantification A. Alberts Ilias Bilionis 39 12 0 18 Jan 2023
Machine Learning for Smart and Energy-Efficient Buildings Hari Prasanna Das Yu-Wen Lin Utkarsha Agwan Lucas Spangher Alex Devonport Yu Yang Ján Drgoňa A. Chong S. Schiavon C. Spanos HAI AI4CE 34 20 0 27 Nov 2022
Utilising physics-guided deep learning to overcome data scarcity Jinshuai Bai Laith Alzubaidi Qingxia Wang E. Kuhl Bennamoun Yuantong T. Gu PINN AI4CE 52 3 0 24 Nov 2022
Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computing Salah A. Faroughi N. Pawar C. Fernandes Maziar Raissi Subasish Das N. Kalantari S. K. Mahjour PINN AI4CE 37 49 0 14 Nov 2022
Investigating and Mitigating Failure Modes in Physics-informed Neural Networks (PINNs) S. Basir PINN AI4CE 31 21 0 20 Sep 2022
PhySRNet: Physics informed super-resolution network for application in computational solid mechanics Rajat Arora AI4CE 35 10 0 30 Jun 2022
Momentum Diminishes the Effect of Spectral Bias in Physics-Informed Neural Networks G. Farhani Alexander Kazachek Boyu Wang 27 6 0 29 Jun 2022
Respecting causality is all you need for training physics-informed neural networks Sizhuang He Shyam Sankaran P. Perdikaris PINN CML AI4CE 53 199 0 14 Mar 2022
Energy networks for state estimation with random sensors using sparse labels Y. Kumar S. Chakraborty 29 0 0 12 Mar 2022
MIONet: Learning multiple-input operators via tensor product Pengzhan Jin Shuai Meng Lu Lu 25 158 0 12 Feb 2022
Physics-informed neural networks for solving parametric magnetostatic problems Andrés Beltrán-Pulido Ilias Bilionis D. Aliprantis 30 34 0 08 Feb 2022
Physics-informed neural networks for modeling rate- and temperature-dependent plasticity Rajat Arora P. Kakkar Biswadip Dey Amit Chakraborty PINN AI4CE 49 19 0 20 Jan 2022
Temperature Field Inversion of Heat-Source Systems via Physics-Informed Neural Networks Xu Liu Wei Peng Zhiqiang Gong Weien Zhou W. Yao 32 54 0 18 Jan 2022
Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next S. Cuomo Vincenzo Schiano Di Cola F. Giampaolo G. Rozza Maizar Raissi F. Piccialli PINN 31 1,190 0 14 Jan 2022
Machine-Learning Identification of Hemodynamics in Coronary Arteries in the Presence of Stenosis M. Farajtabar M. Biglarian Morteza Miansari 16 3 0 02 Nov 2021
Physics and Equality Constrained Artificial Neural Networks: Application to Forward and Inverse Problems with Multi-fidelity Data Fusion S. Basir Inanc Senocak PINN AI4CE 36 68 0 30 Sep 2021
Simulating progressive intramural damage leading to aortic dissection using an operator-regression neural network Minglang Yin Ehsan Ban B. Rego Enrui Zhang C. Cavinato J. Humphrey George Karniadakis AI4CE 34 52 0 25 Aug 2021
Physics-constrained Deep Learning for Robust Inverse ECG Modeling Jianxin Xie B. Yao 30 22 0 26 Jul 2021
PhyCRNet: Physics-informed Convolutional-Recurrent Network for Solving Spatiotemporal PDEs Pu Ren Chengping Rao Yang Liu Jianxun Wang Hao Sun DiffM AI4CE 48 193 0 26 Jun 2021
Long-time integration of parametric evolution equations with physics-informed DeepONets Sizhuang He P. Perdikaris AI4CE 24 117 0 09 Jun 2021
Efficient training of physics-informed neural networks via importance sampling M. A. Nabian R. J. Gladstone Hadi Meidani DiffM PINN 71 225 0 26 Apr 2021
Physics-informed neural networks for the shallow-water equations on the sphere Alexander Bihlo R. Popovych 14 77 0 01 Apr 2021
Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets Sizhuang He Hanwen Wang P. Perdikaris AI4CE 45 671 0 19 Mar 2021
Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data Mohamed Aziz Bhouri P. Perdikaris 31 20 0 04 Mar 2021
Can Transfer Neuroevolution Tractably Solve Your Differential Equations? Jian Cheng Wong Abhishek Gupta Yew-Soon Ong 28 21 0 06 Jan 2021
An overview on deep learning-based approximation methods for partial differential equations C. Beck Martin Hutzenthaler Arnulf Jentzen Benno Kuckuck 35 146 0 22 Dec 2020
On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks Sizhuang He Hanwen Wang P. Perdikaris 135 442 0 18 Dec 2020
Physics-informed neural networks for myocardial perfusion MRI quantification R. L. M. V. Herten A. Chiribiri M. Breeuwer M. Veta C. Scannell 22 43 0 25 Nov 2020
When and why PINNs fail to train: A neural tangent kernel perspective Sizhuang He Xinling Yu P. Perdikaris 33 881 0 28 Jul 2020
NPLIC: A Machine Learning Approach to Piecewise Linear Interface Construction M. Ataei M. Bussmann V. Shaayegan F. Costa Sejin Han Chul B. Park AI4CE 13 18 0 26 Jun 2020
Interface learning of multiphysics and multiscale systems Shady E. Ahmed Omer San Kursat Kara R. Younis Adil Rasheed PINN AI4CE 8 6 0 17 Jun 2020
Physics informed deep learning for computational elastodynamics without labeled data Chengping Rao Hao Sun Yang Liu PINN AI4CE 27 222 0 10 Jun 2020
Deep learning of free boundary and Stefan problems Sizhuang He P. Perdikaris 27 80 0 04 Jun 2020
DiscretizationNet: A Machine-Learning based solver for Navier-Stokes Equations using Finite Volume Discretization Rishikesh Ranade C. Hill Jay Pathak AI4CE 59 123 0 17 May 2020
Physics-informed deep learning for incompressible laminar flows Chengping Rao Hao Sun Yang Liu PINN AI4CE 17 223 0 24 Feb 2020
Understanding and mitigating gradient pathologies in physics-informed neural networks Sizhuang He Yujun Teng P. Perdikaris AI4CE PINN 42 290 0 13 Jan 2020
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 276 5,695 0 05 Dec 2016