Fast-Track Your Simulations: Machine Learning-Enhanced Dynamics for Swift Solver Convergence

What is it about?

Computer simulations are commonly used in scientific modeling and engineering design to solve mathematical equations related to physical problems. However, this process can be computationally intensive and time-consuming. In this study, we present a new method that can significantly speed up the flow simulations that use conventional time-advance schemes. Our approach can be easily integrated into existing flow solvers without requiring any changes to their base implementation. Our acceleration scheme is particularly valuable to researchers who are content with their current flow solver’s performance, yet desire a faster simulation process. Unlike other approaches that may necessitate complex timeadvance schemes or substantial modifications to code implementation, our method is easy to integrate and does not require such changes. By integrating our method into their flow solver, researchers can obtain identical results at an accelerated rate, without sacrificing the precision of their simulations and without needing to make significant changes to their existing code implementation.

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Photo by Google DeepMind on Unsplash

Photo by Google DeepMind on Unsplash