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MARIO: Neural Field Surrogate Modeling for Aerodynamic Applications

This B.Tech Aerospace / Aeronautical Engineering project is based on the recent research direction ‘MARIO: Neural Field Surrogate Modeling for Aerodynamic Applications’. The project focuses on applying artificial intelligence, machine learning, deep…

CFD Projects

MARIO: Neural Field Surrogate Modeling for Aerodynamic Applications — Project Overview

Project Overview This B.Tech Aerospace / Aeronautical Engineering project is based on the recent research direction 'MARIO: Neural Field Surrogate Modeling for Aerodynamic Applications'. The project focuses on applying artificial intelligence, machine learning, deep learning, computer vision, reinforcement learning, surrogate modelling, or RAG-style intelligent assistance to the CFD Projects area. Students can use the linked 2023-onward research paper/source as the academic base, then convert it into an implementation-focused final-year project with a simplified dataset, simulation model, Python workflow, dashboard, or prototype demonstration.
Research Paper Title MARIO: Neural Field Surrogate Modeling for Aerodynamic Applications
Research Paper / PDF Link Open Paper / PDF
Year 2025
Project Area CFD Projects
Project Type Neural Field CFD
Required Tools / Software Python, PyTorch/TensorFlow, NumPy, OpenFOAM/Ansys CFD optional, ParaView, Streamlit
Main Features / Working Principle Use neural-field surrogate ideas to predict aerodynamic simulation fields
Expected Output A neural-field-inspired CFD visualization prototype
Possible Add-ons Add 3D surface field display
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This B.Tech aerospace project resource helps students connect a recent AI-based research direction with a practical implementation plan, tools, expected output, and possible extensions.

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