← 研踪 PaperTrailarXiv:2611.12361 · v1
Law-Invariant Operators for Stochastic Mechanics
首次提交 2026-11-09 21:10
版本更新 2026-11-09 21:10
雷达观测 2026-11-10 15:22
摘要
A central objective in stochastic mechanics is to separate deterministic physical behavior from the probability laws governing uncertain inputs. While classical spectral approaches achieve this separation in linear settings, nonlinear responses introduce interactions beyond conventional second-order variability response functions (VRF). Inspired by the spirit of VRFs, this work establishes that, for a fixed physical system, uncertainty propagation is described by a law-invariant operator mapping input realizations to responses, governed by the underlying physics and the deterministic parameters of the system. Different probability laws affect the input sampling and resulting response statistics, but not the mapping operator. We establish this law invariance under complete, invertible coordinate transformations and develop complementary, Fourier, fixed-reference polynomial-chaos (PCE) and finite element method (FEM) transfer formulations. Fourier captures nonlinear cross-frequency interactions and recovers the classical VRF in the appropriate linear limit, PCE describes coupling among stochastic modes, while FEM expresses the deterministic inverse-stiffness action. Neural approximations of these deterministic operators are investigated through analytical and finite-element benchmarks, examining cross-law generalization, the influence of training distributions and stochastic excitation, and the prediction of response fields and statistics. The results demonstrate the potential for reusing learned physical operators across different stochastic laws without retraining, while highlighting the importance of adequate training coverage and the challenges of accurately estimating low-probability events.
作者备注：31 pages, 19 figures
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v1 · 2026-11-09 21:10 · 最新观测版本
Law-Invariant Operators for Stochastic Mechanics
A central objective in stochastic mechanics is to separate deterministic physical behavior from the probability laws governing uncertain inputs. While classical spectral approaches achieve this separation in linear settings, nonlinear responses introduce interactions beyond conventional second-order variability response functions (VRF). Inspired by the spirit of VRFs, this work establishes that, for a fixed physical system, uncertainty propagation is described by a law-invariant operator mapping input realizations to responses, governed by the underlying physics and the deterministic parameters of the system. Different probability laws affect the input sampling and resulting response statistics, but not the mapping operator. We establish this law invariance under complete, invertible coordinate transformations and develop complementary, Fourier, fixed-reference polynomial-chaos (PCE) and finite element method (FEM) transfer formulations. Fourier captures nonlinear cross-frequency interactions and recovers the classical VRF in the appropriate linear limit, PCE describes coupling among stochastic modes, while FEM expresses the deterministic inverse-stiffness action. Neural approximations of these deterministic operators are investigated through analytical and finite-element benchmarks, examining cross-law generalization, the influence of training distributions and stochastic excitation, and the prediction of response fields and statistics. The results demonstrate the potential for reusing learned physical operators across different stochastic laws without retraining, while highlighting the importance of adequate training coverage and the challenges of accurately estimating low-probability events.
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