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March 2, 20260 citationsOpen Access

PDE Generators from Energy Space Structure in Feynman-Kac Theory

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RFRamiro Fontes

Key Points

  • The research investigates the relationship between energy spaces and the PDE structures of conditional expectations within stochastic calculus.
  • Defined an operator derivative D_X through adjointness to stochastic integrals.
  • Analyzed energy function Gamma^X(t) for Gaussian processes and iterated Brownian motion.
  • Derived PDE structures and coefficients for various stochastic processes, including L^2 for Levy processes.
  • For Gaussian processes, established coefficient (1/2) d/dt Gamma^X(t) for the marginal density PDE.
  • Identified biharmonic generator structure for iterated Brownian motion.
  • Showed that Levy processes lead to nonlocal operators, including the fractional Laplacian.

Abstract

The operator factorization framework for stochastic calculus defines an operator derivative DX via adjointness to the stochastic integral, with energy function GammaX (t) = ||Pi DX Xₜ||² replacing classical quadratic variation. This paper asks: what does the energy space H determine about the PDE structure of conditional expectations? The answer is a structural principle, not a proof technique. For Gaussian processes, GammaX (t) = R (t, t) is deterministic, and the marginal density PDE has coefficient (1/2) d/dt GammaX (t). This recovers classical diffusion operators for Brownian motion and time-dependent diffusivities for fractional Brownian motion, and proves that continuous Gaussian memory can only scale the time derivative — never producing a spatial fractional operator. For iterated Brownian motion, the tensor-product energy space produces a biharmonic generator; we derive the complete distributional PDE: the time derivative of p equals (1/8) times the fourth spatial derivative of p, plus a local-time source term concentrated at the origin. For Levy processes, the jump energy space L² (dt x nu) produces nonlocal operators including the fractional Laplacian. Every PDE derived in this paper can also be obtained by direct methods — density calculations for Gaussian processes, conditional analysis and Fourier methods for iterated Brownian motion, compensator calculations for Levy processes. The operator framework does not replace these proofs; it explains, after the fact, why each energy space produces its particular PDE structure. The genuine proof-mechanical contributions of the operator factorization program — including the cylindrical reduction principle, the Leibniz obstruction for stochastic volatility, and the representability obstruction for jump processes — are developed in the companion papers. The present paper serves as the PDE-side application: it shows that the energy space classification has concrete analytical consequences.

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Cite This Study

Ramiro Fontes (2026) studied this question.

synapsesocial.com/papers/69a52e75f1e85e5c73bf231bhttps://doi.org/10.5281/zenodo.18809542
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1The Operator Derivative in Continuous Stochastic Calculus: A Hilbert Energy Space Framework2026
  2. 2The One-Parameter Banach Factorization for Stable Lévy Processes: Representability Obstructions and Leibniz Defects2026
  3. 3An Ito Formula via Leibniz Defects in Banach Energy Spaces and PDE Generators for Stable Processes2026
  4. 4A Meta‑Dynamical PDE on the Coefficient Space of D‑Operators (One Possible Realization of a "Theory about Theories'')2026
  5. 5The One-Parameter Banach Factorization for Stable Lévy Processes: Representability Obstructions and Leibniz Defects2026