A Multimodal Vision Transformer-based Modeling Framework for Prediction of Fluid Flows in Energy Systems

📰 ArXiv cs.AI

A multimodal vision transformer-based framework predicts fluid flows in energy systems using computational fluid dynamics simulations

advanced Published 6 Apr 2026

Action Steps

Employ a hierarchical Vision Transformer (SwinV2-UNet) architecture
Utilize multimodal inputs to capture complex fluid flow phenomena
Train the model on high-pressure gas injection data
Evaluate the model's performance on predicting fluid flows in energy systems

Who Needs to Know This

This research benefits data scientists and AI engineers working on energy systems and computational fluid dynamics, as it provides a novel approach to predicting fluid flows

Key Insight

💡 Vision Transformers can be used to predict complex fluid flows in energy systems, reducing the need for expensive computational fluid dynamics simulations

Key Takeaways

A multimodal vision transformer-based framework predicts fluid flows in energy systems using computational fluid dynamics simulations

Full Article

Title: A Multimodal Vision Transformer-based Modeling Framework for Prediction of Fluid Flows in Energy Systems

Abstract:
arXiv:2604.02483v1 Announce Type: cross Abstract: Computational fluid dynamics (CFD) simulations of complex fluid flows in energy systems are prohibitively expensive due to strong nonlinearities and multiscale-multiphysics interactions. In this work, we present a transformer-based modeling framework for prediction of fluid flows, and demonstrate it for high-pressure gas injection phenomena relevant to reciprocating engines. The approach employs a hierarchical Vision Transformer (SwinV2-UNet) arc

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