Enforcing semilinearity via local stencil-scale normalization and training on polynomial profiles produces stable, generalizable neural advection schemes with a new flux limiter that improves shape preservation over OSTVD3.
Friedman (Eds.), The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, New York, NY, 2009, pp
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Design principles for stable and generalizable data-driven discretizations for solving linear hyperbolic conservation laws
Enforcing semilinearity via local stencil-scale normalization and training on polynomial profiles produces stable, generalizable neural advection schemes with a new flux limiter that improves shape preservation over OSTVD3.