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New method improves SDE surrogate model accuracy for path-dependent observables

By PulseAugur Editorial · [1 sources] ·

This paper introduces a novel variational loss function for learning surrogate models of stochastic differential equations (SDEs). The proposed goal-oriented learning approach uses an error bound for path-space observables, which standard loss functions often fail to provide. This method is demonstrated to improve accuracy in predicting statistics like first hitting times and exhibits robustness to shifts in data distribution, particularly for overdamped Langevin systems. AI

IMPACT Introduces a novel loss function for SDE surrogate models, potentially improving efficiency and accuracy in simulations for various applications.

RANK_REASON This is a research paper detailing a new methodology for learning surrogate models of SDEs. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New method improves SDE surrogate model accuracy for path-dependent observables

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This is a research paper detailing a new methodology for learning surrogate models of SDEs. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Joanna Zou, Han Cheng Lie, Youssef Marzouk ·

    Goal-oriented learning of stochastic differential equations using error bounds on path-space observables

    arXiv:2603.20467v2 Announce Type: replace-cross Abstract: Stochastic differential equations (SDEs), which serve as the governing equations for dynamical systems in a broad range of applications, can become cost-prohibitive for numerical simulation at scales necessary for quantify…

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