Cover Image

Uncertainty Quantification in Computational Fluid Dynamics: Physics and Machine Learning Based Approaches

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Chu, Minghan
Format:	Preprint
Published:	2023
Subjects:	Fluid Dynamics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Physics Based & Machine Learning Methods For Uncertainty Estimation In Turbulence Modeling
by: Chu, Minghan
Published: (2024)

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

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification
by: Chu, Minghan, et al.
Published: (2025)

A Deep Learning Approach For Epistemic Uncertainty Quantification Of Turbulent Flow Simulations
by: Chu, Minghan, et al.
Published: (2024)

Physics Constrained Deep Learning For Turbulence Model Uncertainty Quantification
by: Chu, Minghan, et al.
Published: (2024)

Convolutional Neural Networks For Turbulent Model Uncertainty Quantification
by: Chu, Minghan, et al.
Published: (2024)

Integrating Uncertainty Quantification into Computational Fluid Dynamics Models of Coronary Arteries Under Steady Flow
by: Usman, Muhammad, et al.
Published: (2025)

Uncertainty Quantification and Flow Dynamics in Rotating Detonation Engines
by: Kumar, Vinay, et al.
Published: (2025)

Developing a Model-Consistent Reduced-Dimensionality training approach to quantify and reduce epistemic uncertainty in separated flows
by: Chu, Minghan
Published: (2024)

HOSVD-SR: A Physics-Based Deep Learning Framework for Super-Resolution in Fluid Dynamics
by: Barragán, Guillermo, et al.
Published: (2025)

OpenFOAMGPT: a RAG-Augmented LLM Agent for OpenFOAM-Based Computational Fluid Dynamics
by: Pandey, Sandeep, et al.
Published: (2025)

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

Fluid Intelligence: A Forward Look on AI Foundation Models in Computational Fluid Dynamics
by: Ashton, Neil, et al.
Published: (2025)

Inpainting Computational Fluid Dynamics with Deep Learning
by: Shu, Dule, et al.
Published: (2024)

Challenges and Opportunities for Machine Learning in Fluid Mechanics
by: Mendez, M. A., et al.
Published: (2022)

Advanced Theoretical Analysis of Stability and Convergence in Computational Fluid Dynamics for Computer Graphics
by: Santos, Rômulo Damasclin Chaves dos
Published: (2024)

Deep Learning-Based Prediction of High Explosive Induced Fluid Dynamics
by: VanGessel, Francis G., et al.
Published: (2025)

Review of Physics-based and Data-driven Multiscale Simulation Methods for Computational Fluid Dynamics and Nuclear Thermal Hydraulics
by: Iskhakov, Arsen S., et al.
Published: (2021)

DDFKs: Fluid Simulation with Dynamic Divergence-Free Kernels
by: Xing, Jingrui, et al.
Published: (2026)

Adjoint-based Particle Forcing Reconstruction and Uncertainty Quantification
by: Domínguez-Vázquez, Daniel, et al.
Published: (2022)

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

Optimal Parallelization Strategies for Active Flow Control in Deep Reinforcement Learning-Based Computational Fluid Dynamics
by: Jia, Wang, et al.
Published: (2024)

Computational Modelling of Thixotropic Multiphase Fluids
by: Espinosa-Moreno, Andres Santiago, et al.
Published: (2025)

Uncertainty Quantification in Resolvent Analysis of Experimental Wall-Bounded Turbulent Flows
by: Gomez, Salvador Rey, et al.
Published: (2025)

OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics
by: Zhang, Rui, et al.
Published: (2025)

Machine Learning in Viscoelastic Fluids via Energy-Based Kernel Embedding
by: Otto, Samuel E., et al.
Published: (2024)

Implementation of Immersed Boundaries via Volume Penalization in the Industrial Aeronautical Computational Fluid Dynamics Solver CODA
by: Nunez, Jonatan, et al.
Published: (2024)

Modeling and Computational Fluid Dynamics Validation of a Nonholonomically Constrained Two-Rigid-Body Swimming System
by: Ardister, Jamal, et al.
Published: (2025)

Transported Memory Networks accelerating Computational Fluid Dynamics
by: Schulz, Matthias, et al.
Published: (2025)

Towards Quantum Machine Learning of Lattice Boltzmann Collision Operators for Fluid Dynamic Simulations
by: Itani, Wael, et al.
Published: (2025)

Uncertainty Quantification for Multi-fidelity Simulations
by: Kumar, Swapnil
Published: (2025)

New Governing Equations for Fluid Dynamics
by: Liu, Chaoqun, et al.
Published: (2021)

Impact of Loss Weight and Model Complexity on Physics-Informed Neural Networks for Computational Fluid Dynamics
by: Chou, Yi En, et al.
Published: (2025)

Uncertainty Quantification of Drag Reduction over Superhydrophobic Surfaces by Unified Parameterizing Structure Spacing
by: Kim, Byeong-Cheon, et al.
Published: (2025)

Probabilistic Eddy Identification with Uncertainty Quantification
by: Covington, Jeffrey, et al.
Published: (2024)

Neural Physics: Using AI Libraries to Develop Physics-Based Solvers for Incompressible Computational Fluid Dynamics
by: Chen, Boyang, et al.
Published: (2024)

A Unified Framework for Total Variation Regularized Optimization in Fluid Dynamics and Related Physical Systems
by: Gupta, Varsha
Published: (2024)

Predictive Model and Optimization of Micromixers Geometry using Gaussian Process with Uncertainty Quantification and Genetic Algorithm
by: Maionchi, Daniela de Oliveira, et al.
Published: (2024)

Quantum Computation of Fluid Dynamics
by: Bharadwaj, Sachin S., et al.
Published: (2020)

Designing an Optimal Scoop for Holloman High-Speed Test Track Water Braking Mechanism using Computational Fluid Dynamics
by: Terrazas, Jose A., et al.
Published: (2024)