mlsanityData to 14 Oct 2026

One model to solve them all: 2BSDE families via neural operators

arXiv 2511.01125 ↗First posted 3 Nov 2025cs.LG cs.NA math.APcited by 4

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Abstract (arXiv)

We introduce a mild generative variant of the classical neural operator model, which leverages Kolmogorov--Arnold networks to solve infinite families of second-order backward stochastic differential equations (2BSDEs) on regular bounded Euclidean domains with random terminal time. Our first main result shows that the solution operator associated with a broad range of 2BSDE families is approximable by appropriate neural operator models. We then identify a structured subclass of (infinite) families of 2BSDEs whose neural operator approximation requires only a polynomial number of parameters in the reciprocal approximation rate, as opposed to the exponential requirement in general worst-case neural operator guarantees.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 48 of its 95 references that are in the map. First posted on arXiv 3 Nov 2025.

Builds on

48 of 95 references.

References · 48

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36 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026Deep Hilbert--Galerkin Methods for Infinite-Dimensional PDEs and Optimal Controlcited by 2
  2. 2026Adaptivity Under Realizability Constraints: Comparing In-Context and Agentic Learningcited by 1
  3. 2026Algorithmic Foundations of Deep Learning: Complexity-Theoretic Rates and a Characterization of Universal Approximationcited by 1
  4. 2026NeuralChaos: Optimal Adapted Approximation of Square Integrable Predictable Processescited by 0