arXiv:2512.13069v2 Announce Type: replace Abstract: Accurate aerodynamic prediction often relies on high-fidelity simulations; however, their prohibitive computational costs severely limit their applicability in data-driven modeling. This limitation motivates the development of multi-fidelity strategies that leverage inexpensive low-fidelity information without compromising accuracy. Addressing this challenge, this work presents a multi-fidelity deep learning framework that combines autoencoder-based transfer learning with a newly developed Multi-Split Conformal Prediction (MSCP) strategy to a
The increasing computational demands of high-fidelity simulations across various scientific and engineering disciplines are driving the development of more efficient data-driven modeling approaches.
This development in multi-fidelity deep learning can significantly accelerate R&D cycles by reducing computational costs for complex simulations, particularly in fields like aerospace and fluid dynamics.
The ability to leverage inexpensive low-fidelity data without sacrificing accuracy provides a new pathway for integrating AI into computationally intensive design and analysis processes.
- · Aerospace Industry
- · Computational Fluid Dynamics Researchers
- · AI/ML Software Developers
- · Engineering Design firms
- · Traditional high-fidelity simulation software providers (without AI integration)
- · Organizations reliant solely on computationally expensive design cycles
Faster and cheaper development of complex engineered systems becomes possible due to efficient data fusion.
This could lead to a proliferation of more sophisticated designs, rapid prototyping, and optimized performance across industries.
The reduced barrier to advanced simulation might democratize access to high-fidelity modeling, fostering innovation in smaller firms and research groups.
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Read at arXiv cs.LG