GNNs on hit graphs outperform classical methods for neutral-hadron energy and muon/hadron ID in a proposed EIC hKLM calorimeter, with a 20x faster optical simulation and multi-objective design tradeoff analysis.
Pointwise convergence problem of Ostrovsky equation with rough data and random data
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abstract
In this paper, we consider the pointwise convergence problem of free Ostrovsky equation with rough data and random data. Firstly, we show the almost everywhere pointwise convergence of free Ostrovsky equation in $H^{s}(\mathbb{R})$ with $s\geq \frac{1}{4}$ with rough data. Secondly, we present counterexamples showing that the maximal function estimate related to the free Ostrovsky equation can fail if $s<\frac{1}{4}$. Finally, for every $x\in \mathbb{R}$, we show the almost surely pointwise convergence of free Ostrovsky equation in $L^{2}(\mathbb{R})$ with random data. The main tools are the density theorem, high-low frequency idea, Wiener decomposition and Lemmas 2.1-2.6 as well as the probabilistic estimates of some random series which are just Lemmas 3.2-3.4 in this paper. The main difficulty is that zero is the singular point of the phase functions of free Ostrovsky equation. We use high-low frequency idea to conquer the difficulties.
fields
physics.ins-det 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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ML for the hKLM at the 2nd Detector
GNNs on hit graphs outperform classical methods for neutral-hadron energy and muon/hadron ID in a proposed EIC hKLM calorimeter, with a 20x faster optical simulation and multi-objective design tradeoff analysis.