A semi-discrete scheme for the Stochastic Landau-Lifshitz equation
by François Alouges, Anne de Bouard, Antoine Hocquet
Year:
2014
Publication:
Stochastic PDEs: Analysis and Computations
Abstract:
We propose a new convergent time semi-discrete scheme for the stochastic Landau–Lifshitz–Gilbert equation. The scheme is only linearly implicit and does not require the resolution of a nonlinear problem at each time step. Using a martingale approach, we prove the convergence in law of the scheme up to a subsequence.
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Brief introduction of the dida co-author(s) and relevance for dida's ML developments.
Antoine Hocquet (PhD)
Antoine holds a PhD in applied mathematics from École Polytechnique and spent eight years as a postdoctoral researcher at TU Berlin, working on stochastic PDEs, rough paths theory, and mean-field models, areas where rigorous mathematics try matching messy real-world phenomena. During this period he authored 15+ peer-reviewed publications and taught graduate-level courses in numerical analysis and stochastic methods. Drawn by the challenge of making mathematics work rather than just exist, he then moved into machine learning, building a portfolio spanning image segmentation, reinforcement learning, and LLM-powered document processing. At dida, he works as a machine learning scientist.