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Found 5 papers, 1 papers with code
A Competitive Learning Approach for Specialized Models: A Solution for Complex Physical Systems with Distinct Functional Regimes
Complex systems in science and engineering sometimes exhibit behavior that changes across different regimes.
Application of an automated machine learning-genetic algorithm (AutoML-GA) coupled with computational fluid dynamics simulations for rapid engine design optimization
In the vicinity of the design optimum, the solution is refined by repeatedly running CFD simulations at the projected optimum and adding the newly obtained data to the training dataset.
A novel machine learning-based optimization algorithm (ActivO) for accelerating simulation-driven engine design
The weak learner is used to identify promising regions within the design space to explore, while the strong learner is used to determine the exact location of the optimum within promising regions.
Efficient bifurcation and parameterization of multi-dimensional combustion manifolds using deep mixture of experts: an a priori study
This approach relies on the mixture of experts (MoE) framework, where each neural network is trained to be specialized in a given portion of the input space.
Fluid Dynamics
The Stabilized Explicit Variable-Load Solver with Machine Learning Acceleration for the Rapid Solution of Stiff Chemical Kinetics
As the size and complexity of the simulations grow, so does the need for computational power and time.
