ArticleComputer methods in applied mechanics and engineering2021
Non-invasive Inference of Thrombus Material Properties with Physics-Informed Neural Networks.
Article in Computer methods in applied mechanics and engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Article
- Artificial intelligence for design strategies of tissue engineering materials.Fundamental research · 2026Review
- Physics-Informed Neural Network with Residual Correction Architecture for Hybrid Feedforward-Feedback Temperature Control of DFB Semiconductor Lasers.Sensors (Basel, Switzerland) · 2026Article
- Left atrial flow and thrombosis risk from 4D CT contrast dynamics by physics-informed neural network and indicator dilution theory.bioRxiv : the preprint server for biology · 2026Article
- A Diffusion-Based Digital Twin for Predicting Road Deterioration under Climate and Traffic Stress.Research (Washington, D.C.) · 2026Article
- A multi-agentic framework for real-time, autonomous freeform metasurface design.Science advances · 2025Article
- From PINNs to PIKANs: recent advances in physics-informed machine learning.Machine learning for computational science and engineering · 2025Article
- A novel computational method to predict hypoattenuated leaflet thickening post-transcatheter aortic valve replacement using preprocedural computed tomography scans.JTCVS structural and endovascular · 2025Article
- Super-resolution left ventricular flow and pressure mapping by Navier-Stokes-informed neural networks.Computers in biology and medicine · 2025Article
- A data-consistent model of the last glaciation in the Alps achieved with physics-driven AI.Nature communications · 2025Article
- SELFNet: Denoising Shear Wave Elastography Using Spatial-temporal Fourier Feature Networks.Ultrasound in medicine & biology · 2024Article
- Rapid estimation of left ventricular contractility with a physics-informed neural network inverse modeling approach.Artificial intelligence in medicine · 2024Article
- Review of Machine Learning Techniques in Soft Tissue Biomechanics and Biomaterials.Cardiovascular engineering and technology · 2024Review
- Super-resolution Left Ventricular Flow and Pressure Mapping by Navier-Stokes-Informed Neural Networks.bioRxiv : the preprint server for biology · 2024Article
- Accelerated simulation methodologies for computational vascular flow modelling.Journal of the Royal Society, Interface · 2024Review
- The Potential of Deep Learning to Advance Clinical Applications of Computational Biomechanics.Bioengineering (Basel, Switzerland) · 2023Review
- Recent developments in modeling, imaging, and monitoring of cardiovascular diseases using machine learning.Biophysical reviews · 2023Review
- Machine Learning and the Conundrum of Stroke Risk Prediction.Arrhythmia & electrophysiology review · 2023Review
- Interfacing finite elements with deep neural operators for fast multiscale modeling of mechanics problems.Computer methods in applied mechanics and engineering · 2022Article
- G2Φnet: Relating genotype and biomechanical phenotype of tissues with deep learning.PLoS computational biology · 2022Article
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Authors and funding
4 authors.
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Abstract
We employ physics-informed neural networks (PINNs) to infer properties of biological materials using synthetic data. In particular, we successfully apply PINNs on inferring permeability and viscoelastic modulus from thrombus deformation data, which can be described by the fourth-order Cahn-Hilliard and Navier-Stokes Equations. In PINNs, the partial differential equations are encoded into a loss function, where partial derivatives can be obtained through automatic differentiation (AD). In addition to tackling the challenge of calculating the fourth-order derivative in the Cahn-Hilliard equation with AD, we introduce an auxiliary network along with the main neural network to approximate the second-derivative of the energy potential term. Our model can simultaneously predict unknown material parameters and velocity, pressure, and deformation gradient fields by merely training with partial information among all data, i.e., phase field and pressure measurements, while remaining highly flexible in sampling within the spatio-temporal domain for data acquisition. We validate our model by numerical solutions from the spectral/
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.