Cover Image

Machine Learning Framework for Thrombosis Risk Prediction in Rotary Blood Pumps

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Blum, Christopher, Neidlin, Michael
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Fluid Dynamics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Graph Deep Learning for Intracranial Aneurysm Blood Flow Simulation and Risk Assessment
by: Garnier, Paul, et al.
Published: (2025)

Physics-Based Machine Learning Closures and Wall Models for Hypersonic Transition-Continuum Boundary Layer Predictions
by: Nair, Ashish S., et al.
Published: (2025)

Predicting Ship Responses in Different Seaways using a Generalizable Force Correcting Machine Learning Method
by: Marlantes, Kyle E., et al.
Published: (2024)

Component-Based Machine Learning for Indoor Flow and Temperature Fields Prediction Latent Feature Aggregation and Flow Interaction
by: Wang, Shaofan, et al.
Published: (2025)

Coupling Machine Learning Local Predictions with a Computational Fluid Dynamics Solver to Accelerate Transient Buoyant Plume Simulations
by: Caron, Clément, et al.
Published: (2024)

Solving the Discretised Multiphase Flow Equations with Interface Capturing on Structured Grids Using Machine Learning Libraries
by: Chen, Boyang, et al.
Published: (2024)

Machine Learning Techniques for Data Reduction of CFD Applications
by: Lee, Jaemoon, et al.
Published: (2024)

Physics-Guided Machine Learning for Uncertainty Quantification in Turbulence Models
by: Chu, Minghan, et al.
Published: (2025)

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning
by: Ohana, Ruben, et al.
Published: (2024)

Machine Learning Optimized Approach for Parameter Selection in MESHFREE Simulations
by: Banerjee, Paulami, et al.
Published: (2024)

Accelerating Transient CFD through Machine Learning-Based Flow Initialization
by: Sharpe, Peter, et al.
Published: (2025)

Model-Agnostic AI Framework with Explicit Time Integration for Long-Term Fluid Dynamics Prediction
by: Yang, Sunwoong, et al.
Published: (2024)

NeuralFoil: An Airfoil Aerodynamics Analysis Tool Using Physics-Informed Machine Learning
by: Sharpe, Peter, et al.
Published: (2025)

Combining Machine Learning with Computational Fluid Dynamics using OpenFOAM and SmartSim
by: Maric, Tomislav, et al.
Published: (2024)

Predicting Onflow Parameters Using Transfer Learning for Domain and Task Adaptation
by: Yilmaz, Emre, et al.
Published: (2025)

A Physics-Informed Spatiotemporal Deep Learning Framework for Turbulent Systems
by: Menicali, Luca, et al.
Published: (2025)

High-fidelity Modeling of Full-scale Pressurized Water Reactor Flow Fields for Machine Learning Applications
by: Burnett, Logan A., et al.
Published: (2026)

An Adaptive Machine Learning Framework for Fluid Flow in Dual-Network Porous Media
by: Maduri, V. S., et al.
Published: (2026)

Physics-Guided Actor-Critic Reinforcement Learning for Swimming in Turbulence
by: Koh, Christopher, et al.
Published: (2024)

Intelligent Optimization of Multi-Parameter Micromixers Using a Scientific Machine Learning Framework
by: Hassanzadeh, Meraj, et al.
Published: (2025)

A Two-Phase Deep Learning Framework for Adaptive Time-Stepping in High-Speed Flow Modeling
by: Helwig, Jacob, et al.
Published: (2025)

Reducing Spatial Discretization Error on Coarse CFD Simulations Using an OpenFOAM-Embedded Deep Learning Framework
by: Gonzalez-Sieiro, Jesus, et al.
Published: (2024)

Prediction of Ultrasonic Guided Wave Propagation in Solid-fluid and their Interface under Uncertainty using Machine Learning
by: De, Subhayan, et al.
Published: (2021)

Anisotropic Permeability Tensor Prediction from Porous Media Microstructure via Physics-Informed Progressive Transfer Learning with Hybrid CNN-Transformer
by: Nooraiepour, Mohammad
Published: (2026)

Predicting Flow Dynamics using Diffusion Models
by: Gachnang, Yannick, et al.
Published: (2025)

Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrology
by: Xu, Qingsong, et al.
Published: (2023)

Machine-learning-based multipoint optimization of fluidic injection parameters for improving nozzle performance
by: Yang, Yunjia, et al.
Published: (2024)

Predicting fluid-structure interaction with graph neural networks
by: Gao, Rui, et al.
Published: (2022)

Characterizing Intraventricular Flow Patterns via Modal Decomposition Techniques in Idealized Left Ventricle Models
by: Lazpita, Eneko, et al.
Published: (2025)

Binned Spectral Power Loss for Improved Prediction of Chaotic Systems
by: Chakraborty, Dibyajyoti, et al.
Published: (2025)

Signal-Aware Conditional Diffusion Surrogates for Transonic Wing Pressure Prediction
by: Francés-Belda, Víctor, et al.
Published: (2026)

DeepLag: Discovering Deep Lagrangian Dynamics for Intuitive Fluid Prediction
by: Ma, Qilong, et al.
Published: (2024)

JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics Framework
by: Toshev, Artur P., et al.
Published: (2024)

Predicting Time-Dependent Flow Over Complex Geometries Using Operator Networks
by: Rabeh, Ali, et al.
Published: (2025)

Predicting Energy Budgets in Droplet Dynamics: A Recurrent Neural Network Approach
by: de Aguiar, Diego A., et al.
Published: (2024)

Towards a Foundation-Model Paradigm for Aerodynamic Prediction in Three-dimensional Design
by: Yang, Yunjia, et al.
Published: (2026)

Conditional Neural Field based Reduced Order Model for Dynamic Ditching Load Prediction
by: Schwarz, Henning, et al.
Published: (2026)

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries
by: Rabeh, Ali, et al.
Published: (2024)

Knowledge-Based Convolutional Neural Network for the Simulation and Prediction of Two-Phase Darcy Flows
by: Elabid, Zakaria, et al.
Published: (2024)

An ALE-Consistent Graph Neural Operator-Transformer Framework for Fluid-Structure Interaction
by: Zhao, Shihang, et al.
Published: (2026)