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This paper introduces MuFiNNs, a hierarchical multi-fidelity neural network framework for predicting 3D flame wrinkling and turbulent burning velocity using sparse experimental data. The approach integrates low-fidelity physical trends with high-fidelity corrections to enable robust prediction and extrapolation in data-limited combustion regimes.
This paper introduces Dynamical Physics-Modeled Neural Networks (DynPMNNs), a continuous-time deep learning architecture where hidden layers are defined by ordinary differential equations. It presents a biologically inspired approach grounded in Reproducing Kernel Banach Spaces, demonstrating competitive performance on the California Housing dataset with fewer parameters than standard Neural ODEs.
This paper proposes a new architecture that augments Flux Neural Operators with recurrent Vision Transformers to solve conservation laws as a foundation model. It demonstrates robust generalization and long-time prediction capabilities across diverse conservative systems without explicit access to governing equations.