Local and global well-posedness results are shown for the fractional rough Burgers equation in H^s on the torus for specified ranges of the fractional dissipation parameter gamma, with para-controlled solutions for lower dissipation.
Brownian motion, martingales, and stochastic calculus
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Lecture notes unify stochastic calculus, generator matching, and finite-sample Wasserstein guarantees for continuous-time Markovian generative models.
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Results of Fractional Rough Burgers equation in $H^s$ space and its application
Local and global well-posedness results are shown for the fractional rough Burgers equation in H^s on the torus for specified ranges of the fractional dissipation parameter gamma, with para-controlled solutions for lower dissipation.
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Statistical Analysis of Markovian Generative Modeling
Lecture notes unify stochastic calculus, generator matching, and finite-sample Wasserstein guarantees for continuous-time Markovian generative models.