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版本更新 · v3arXiv:2605.00950

Equation-Free Digital Twins for Nonlinear Structural Dynamics

Mohammad Mahdi Abaei, Ahmad BahooToroody, Arttu Polojärvi, Heikki Remes, Ulf Tyge Tygesen, Mikko Suominen, Michael Beer

原文摘要

Monitoring floating offshore wind turbines (FOWTs) under stochastic excitation and sensor failure requires computationally efficient and physically interpretable estimators. This paper introduces an equation-free, output-only digital twin framework combining Hankel dynamic mode decomposition (Hankel-DMD), sparse-sensor reconstruction, and a stochastic Kalman extension. A sufficient identifiability condition is derived for modal amplitudes recovered from delay-embedded measurements, while Tikhonov regularization addresses ill-conditioned sensor layouts. Correlation-domain identification improves damping estimates under stochastic forcing and distinguishes structural modes from rotor harmonics. The framework is validated using OpenFAST simulations of an NREL 5-MW spar turbine and experimental basin-test measurements from a 1:70-scale IEA 15-MW semi-submersible. In simulations, the identified tower modes agree with established operational modal analysis methods (MAC at least 0.99), while the Kalman estimator achieves R-squared values of 0.91 to 0.99 under severe sensor sparsity. Experimental results identify tower modes at 0.430 and 0.415 Hz with MAC of 1.00 against SSI-COV and reconstruct tower-base bending moments on unseen runs with R-squared values of 0.83 to 0.89 using three of four sensors. The framework provides a computationally efficient, sensor-failure-resilient structural-state estimation layer for digital twins.

eess.SP · cs.LG · eess.SY

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新论文 · v1arXiv:2611.02637

Variable names provide priors: semantic grounding enables data-efficient numerical prediction by large language models

Xin-Rui Ma, Shi-Zhi Chen, Hai-Yan Jiang, De-Cheng Feng, Michael Beer

原文摘要

Large language models (LLMs) have achieved strong performance across diverse tasks, from question answering to code generation. Recent studies have found that LLMs can perform numerical prediction on tabular datasets via in-context learning. However, it remains unclear under what conditions and how LLMs use variable names and in-context examples for numerical prediction. Here we show that semantic grounding, the match between variable names and the values they describe, allows five LLMs without task-specific training to match or outperform seven supervised machine learning (ML) models across 20 datasets from nine scientific domains, particularly when examples are scarce. Reasoning traces indicate that variable names allow LLMs to draw on domain-specific knowledge acquired during pretraining. Using controlled tasks based on physical formulas, we further show that LLMs integrate domain-specific knowledge with in-context examples in a way consistent with Bayesian updating. Specifically, when the relationship between the inputs and the target cannot be inferred from the examples alone, LLMs rely on domain-specific knowledge as the prior. Conversely, as contradicting examples accumulate, LLM predictions shift progressively towards these examples, which act as the likelihood. Our work reveals how LLMs use domain-specific knowledge in numerical prediction and positions them as data-efficient predictors for data-scarce scientific domains.

cs.AI · cs.CL · cs.LG

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