arXiv:2606.19562v1 Announce Type: new Abstract: This chapter reviews recent advances in Scientific Machine Learning (SciML) for modeling coupled fluid flow and transport phenomena governed by the incompressible Navier-Stokes and scalar transport equations. Such systems, found in applications like turbidity currents and thermal convection, feature strong nonlinear coupling and multiscale behavior that make high-fidelity simulations computationally expensive. To address this, the chapter surveys state-of-the-art SciML methods for building efficient surrogate models, including linear reduced-orde
The increasing complexity and computational demands of simulating real-world physical systems are driving a critical need for more efficient modeling approaches.
This development in Scientific Machine Learning significantly reduces the computational expense of high-fidelity simulations for critical applications, accelerating scientific discovery and engineering solutions.
Traditional computational fluid dynamics and transport modeling will be increasingly augmented or replaced by AI-driven surrogate models, leading to faster research cycles and more efficient resource allocation.
- · AI/ML researchers
- · Engineering sectors (aerospace, automotive)
- · Climate modeling institutions
- · Energy sector
- · Traditional high-performance computing providers (for pure simulation)
- · Companies reliant on slow, iterative physical prototyping
SciML will enable faster and more accurate design and optimization of complex systems across multiple industries.
Reduced simulation times could accelerate the development of new materials, energy systems, and climate mitigation strategies.
The democratization of advanced simulation capabilities through SciML might level the playing field for smaller research groups and startups, fostering innovation.
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