A Multimodal Vision Transformer-based Modeling Framework for Prediction of Fluid Flows in Energy Systems
Fuente:
arXiv
Saved in:
| Main Authors: | Yalamanchi, Kiran, Barwey, Shivam, Jarrah, Ibrahim, Pal, Pinaki |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Mesh-based Super-resolution of Detonation Flows with Multiscale Graph Transformers
by: Barwey, Shivam, et al.
Published: (2025)
by: Barwey, Shivam, et al.
Published: (2025)
Understanding Latent Timescales in Neural Ordinary Differential Equation Models for Advection-Dominated Dynamical Systems
by: Nair, Ashish S., et al.
Published: (2024)
by: Nair, Ashish S., et al.
Published: (2024)
Mesh-based Super-Resolution of Fluid Flows with Multiscale Graph Neural Networks
by: Barwey, Shivam, et al.
Published: (2024)
by: Barwey, Shivam, et al.
Published: (2024)
Jacobian-Scaled K-means Clustering for Physics-Informed Segmentation of Reacting Flows
by: Barwey, Shivam, et al.
Published: (2023)
by: Barwey, Shivam, et al.
Published: (2023)
FIGNN: Feature-Specific Interpretability for Graph Neural Network Surrogate Models
by: Raut, Riddhiman, et al.
Published: (2025)
by: Raut, Riddhiman, et al.
Published: (2025)
Chemical Timescale Effects on Detonation Convergence
by: Barwey, Shivam, et al.
Published: (2024)
by: Barwey, Shivam, et al.
Published: (2024)
Interpretable A-posteriori Error Indication for Graph Neural Network Surrogate Models
by: Barwey, Shivam, et al.
Published: (2023)
by: Barwey, Shivam, et al.
Published: (2023)
Towards an AI Fluid Scientist: LLM-Powered Scientific Discovery in Experimental Fluid Mechanics
by: Feng, Haodong, et al.
Published: (2025)
by: Feng, Haodong, et al.
Published: (2025)
Surrogate Model for Heat Transfer Prediction in Impinging Jet Arrays using Dynamic Inlet/Outlet and Flow Rate Control
by: Vaillant, Mikael, et al.
Published: (2025)
by: Vaillant, Mikael, et al.
Published: (2025)
NeuralFluid: Neural Fluidic System Design and Control with Differentiable Simulation
by: Li, Yifei, et al.
Published: (2024)
by: Li, Yifei, et al.
Published: (2024)
An AMReX-based Compressible Reacting Flow Solver for High-speed Reacting Flows relevant to Hypersonic Propulsion
by: Sharma, Shivank, et al.
Published: (2024)
by: Sharma, Shivank, et al.
Published: (2024)
Multiscale Physics-Informed Neural Network for Complex Fluid Flows with Long-Range Dependencies
by: Kumar, Prashant, et al.
Published: (2026)
by: Kumar, Prashant, et al.
Published: (2026)
Modeling Continuous Spatial-temporal Dynamics of Turbulent Flow with Test-time Refinement
by: Chen, Shengyu, et al.
Published: (2024)
by: Chen, Shengyu, et al.
Published: (2024)
The Principle of Minimum Pressure Gradient: An Alternative Basis for Physics-Informed Learning of Incompressible Fluid Mechanics
by: Alhussein, Hussam, et al.
Published: (2024)
by: Alhussein, Hussam, et al.
Published: (2024)
FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes
by: Ramos, David, et al.
Published: (2026)
by: Ramos, David, et al.
Published: (2026)
AI CFD Scientist: Toward Open-Ended Computational Fluid Dynamics Discovery with Physics-Aware AI Agents
by: Somasekharan, Nithin, et al.
Published: (2026)
by: Somasekharan, Nithin, et al.
Published: (2026)
One Scale at a Time: Scale-Autoregressive Modeling for Fluid Flow Distributions
by: Lino, Mario, et al.
Published: (2026)
by: Lino, Mario, et al.
Published: (2026)
A note on the error analysis of data-driven closure models for large eddy simulations of turbulence
by: Chakraborty, Dibyajyoti, et al.
Published: (2024)
by: Chakraborty, Dibyajyoti, et al.
Published: (2024)
PiRD: Physics-informed Residual Diffusion for Flow Field Reconstruction
by: Shan, Siming, et al.
Published: (2024)
by: Shan, Siming, et al.
Published: (2024)
Data-Efficient Deep Operator Network for Unsteady Flow: A Multi-Fidelity Approach with Physics-Guided Subsampling
by: Yang, Sunwoong, et al.
Published: (2025)
by: Yang, Sunwoong, et al.
Published: (2025)
Navigation in a Three-Dimensional Urban Flow using Deep Reinforcement Learning
by: Tonti, Federica, et al.
Published: (2025)
by: Tonti, Federica, et al.
Published: (2025)
Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators
by: Alkin, Benedikt, et al.
Published: (2024)
by: Alkin, Benedikt, et al.
Published: (2024)
Operator Learning for Reconstructing Flow Fields from Sparse Measurements: an Energy Transformer Approach
by: Zhang, Qian, et al.
Published: (2025)
by: Zhang, Qian, et al.
Published: (2025)
Spatio-Temporal Fluid Dynamics Modeling via Physical-Awareness and Parameter Diffusion Guidance
by: Wu, Hao, et al.
Published: (2024)
by: Wu, Hao, et al.
Published: (2024)
Fine-tuning a Large Language Model for Automating Computational Fluid Dynamics Simulations
by: Dong, Zhehao, et al.
Published: (2025)
by: Dong, Zhehao, et al.
Published: (2025)
HydroGym: A Reinforcement Learning Platform for Fluid Dynamics
by: Lagemann, Christian, et al.
Published: (2025)
by: Lagemann, Christian, et al.
Published: (2025)
Enhancing Graph U-Nets for Mesh-Agnostic Spatio-Temporal Flow Prediction
by: Yang, Sunwoong, et al.
Published: (2024)
by: Yang, Sunwoong, et al.
Published: (2024)
Data-Free PINNs for Compressible Flows: Mitigating Spectral Bias and Gradient Pathologies via Mach-Guided Scaling and Hybrid Convolutions
by: Yano, Ryosuke
Published: (2026)
by: Yano, Ryosuke
Published: (2026)
Laminar-to-Turbulent Transition of Yield-Stress Fluids in Pipe and Channel Flows
by: Prajapati, Shivam, et al.
Published: (2026)
by: Prajapati, Shivam, et al.
Published: (2026)
OptMetaOpenFOAM: Large Language Model Driven Chain of Thought for Sensitivity Analysis and Parameter Optimization based on CFD
by: Chen, Yuxuan, et al.
Published: (2025)
by: Chen, Yuxuan, et al.
Published: (2025)
Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction
by: Oommen, Vivek, et al.
Published: (2025)
by: Oommen, Vivek, et al.
Published: (2025)
Surrogate models for Rock-Fluid Interaction: A Grid-Size-Invariant Approach
by: Pinheiro, Nathalie C., et al.
Published: (2026)
by: Pinheiro, Nathalie C., et al.
Published: (2026)
Learning Distributions of Complex Fluid Simulations with Diffusion Graph Networks
by: Lino, Mario, et al.
Published: (2025)
by: Lino, Mario, et al.
Published: (2025)
An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning
by: Abadía-Heredia, Rodrigo, et al.
Published: (2025)
by: Abadía-Heredia, Rodrigo, et al.
Published: (2025)
PT-PINNs: A Parametric Engineering Turbulence Solver based on Physics-Informed Neural Networks
by: Jiang, Liang, et al.
Published: (2025)
by: Jiang, Liang, et al.
Published: (2025)
A Spectral Element Enrichment Wall-Model
by: Brill, Steven R., et al.
Published: (2024)
by: Brill, Steven R., et al.
Published: (2024)
PAINT: Parallel-in-time Neural Twins for Dynamical System Reconstruction
by: Radler, Andreas, et al.
Published: (2025)
by: Radler, Andreas, et al.
Published: (2025)
LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements
by: Hetherington, Ashton, et al.
Published: (2024)
by: Hetherington, Ashton, et al.
Published: (2024)
MetaOpenFOAM: an LLM-based multi-agent framework for CFD
by: Chen, Yuxuan, et al.
Published: (2024)
by: Chen, Yuxuan, et al.
Published: (2024)
DSO: Dual-Scale Neural Operators for Stable Long-term Fluid Dynamics Forecasting
by: Dong, Huanshuo, et al.
Published: (2026)
by: Dong, Huanshuo, et al.
Published: (2026)
Similar Items
-
Mesh-based Super-resolution of Detonation Flows with Multiscale Graph Transformers
by: Barwey, Shivam, et al.
Published: (2025) -
Understanding Latent Timescales in Neural Ordinary Differential Equation Models for Advection-Dominated Dynamical Systems
by: Nair, Ashish S., et al.
Published: (2024) -
Mesh-based Super-Resolution of Fluid Flows with Multiscale Graph Neural Networks
by: Barwey, Shivam, et al.
Published: (2024) -
Jacobian-Scaled K-means Clustering for Physics-Informed Segmentation of Reacting Flows
by: Barwey, Shivam, et al.
Published: (2023) -
FIGNN: Feature-Specific Interpretability for Graph Neural Network Surrogate Models
by: Raut, Riddhiman, et al.
Published: (2025)