experimental model updating

This study addresses the persistent challenge of significant discrepancies between physics-based simulation models and real-world measurements, which often arise from modeling biases that are non-local and dependent on sensor placement, thereby hindering targeted correction. To overcome this, the authors propose a non-intrusive and interpretable approach that leverages a Gaussian mixture model within a Bayesian framework, combined with the Expectation-Maximization (EM) algorithm, to cluster sensor data and automatically identify physically meaningful parameter groups. This enables the discovery of systematic bias patterns without altering the original model structure, ensuring physically consistent and generalizable model calibration. The method successfully identifies key sources of discrepancy in both numerical experiments and a real-world case study involving thermal conduction in a concrete bridge, effectively guiding precise refinement of the underlying physical model.

To address the scarcity of calibration data and the tendency of conventional methods to overlook critical information in physics-based modeling, this paper proposes a Bayesian adaptive experimental design framework that jointly optimizes calibration parameter estimation and simulation point selection. The method dynamically selects the most informative simulation points within a batch-sequential process, significantly reducing the number of required simulation evaluations. Its key contributions are: (i) the first application of maximizing the variational lower bound on expected information gain (EIG) for joint inference and design; and (ii) the use of Gaussian processes to jointly model the simulator response, observational noise, and unknown calibration parameters—thereby capturing their intrinsic couplings. Experiments on both synthetic and real-world physical systems demonstrate substantial improvements in calibration accuracy and data efficiency over fixed-design machine learning approaches.

This paper addresses the reliability of calibration evaluation for machine learning models, identifying systematic biases in the widely used Expected Calibration Error (ECE) under distributional shift and varying binning strategies. Methodologically, it clarifies the logical hierarchy among multi-level calibration definitions, and systematically exposes ECE’s limitations through visualization, binning-based statistical analysis, and theoretical derivation—demonstrating its failure to satisfy key requirements of robustness and consistency in calibration assessment. Building on this critique, the paper introduces and explicates emerging calibration paradigms—including distribution-level and instance-level calibration—alongside their corresponding evaluation methodologies, thereby constructing a rigorous, interpretable, and practice-oriented calibration knowledge framework. The results equip researchers with principled guidance for selecting appropriate evaluation metrics and advance calibration assessment from ad hoc, heuristic practices toward standardization and formalization.

This study addresses the challenge of jointly modeling calibration and control parameters in computer model calibration, where the distribution of calibration parameters is unknown while that of control parameters is known. To tackle this issue, the authors propose a nonparametric Bayesian calibration method based on measure decomposition. The approach preserves the known marginal distribution of the control parameters while employing stochastic process modeling and Bayesian inference to construct a posterior distribution over the input space that aligns with field observations. Notably, this work is the first within a nonparametric calibration framework to explicitly maintain the prior distributional properties of the control parameters, thereby substantially enhancing the physical consistency and scientific credibility of the calibration results.

In model-based systems engineering, low experimental data reuse efficiency and excessive redundant experiments hinder digital engineering agility. To address this, this paper proposes a case-based reasoning (CBR)-driven experimental management framework that explicitly integrates domain knowledge. The framework features structured experimental metadata modeling, digital twin–enabled scenario semantic alignment, and an interpretable similarity assessment mechanism to intelligently determine whether historical experiments can be transferred to address new verification queries. Its key innovation lies in embedding domain knowledge explicitly into both the CBR retrieval and adaptation stages, thereby enabling trustworthy cross-operating-condition and cross-configuration experimental data reuse. Evaluated on an industrial-scale vehicle energy system design case, the framework reduces redundant experiments by 37% and shortens early verification cycles by 42% on average, significantly enhancing iterative efficiency in digital engineering and advancing intelligent experimental management.

This study addresses the limitation that single observation mode evaluations fail to capture performance fluctuations in physical field reconstruction by proposing an integrated benchmark platform. The platform incorporates seven types of partial differential equation data with configurable observation operators, enabling parameterized mode definitions through the decoupling of observation construction from physical records, and establishes a standardized cross-mode evaluation protocol to quantify model sensitivity. Experiments reveal that cross-mode errors consistently exceed matched-mode errors, and dense observations do not necessarily reduce errors. Furthermore, mixed training strategies effectively mitigate transfer errors. This work provides a systematic tool and novel insights for the robustness evaluation of physical field reconstruction.

This work addresses Bayesian optimal experimental design under computationally expensive models with limited design evaluations. It proposes an adaptive sequential elimination algorithm that significantly reduces the variance and computational cost of nested Monte Carlo estimators by reusing parameter samples, employing common random numbers, and applying Rao–Blackwellization. A bootstrap-based probabilistic comparison mechanism is integrated to iteratively eliminate inferior designs. The method achieves high reliability while drastically reducing the number of model evaluations, making it well-suited for large-scale engineering applications where computational efficiency and decision accuracy must be carefully balanced.

This study addresses the challenge of adaptively determining when to reset a model’s structure under lightweight parameter update strategies to balance predictive accuracy, computational cost, and stability. The authors propose a “model specification debt” mechanism that accumulates evidence—such as prediction score discrepancies, stacked weights, or calibration diagnostics—to formulate a cost-sensitive trigger rule for model resetting. This framework generalizes fixed-interval updating as a special case and enables flexible deployment in open environments. Evaluated on the M4 dataset, the approach achieves predictive accuracy comparable to full retraining while consuming only 28% of the computation time, significantly reducing instability. It consistently matches or outperforms fixed-update strategies across diverse scenarios and offers dynamic, evidence-driven adaptation capabilities.

This work addresses the vulnerability of surrogate models in digital twins to concept drift under shifting operational conditions, which degrades both predictive accuracy and uncertainty quantification. To mitigate this, the authors propose an adaptive digital twin framework that integrates multivariate distribution drift detection based on Fisher scores, a parameter-efficient LoRA-based continual learning mechanism for model adaptation, and Mann-Whitney U test–driven online statistical validation to assess the necessity of updates, enable efficient fine-tuning, and ensure reliability. Evaluated on a stochastic linear system and a directed energy deposition additive manufacturing task, the approach significantly accelerates drift detection, enhances model recovery accuracy, and improves the quality of uncertainty estimates, thereby enabling trustworthy continuous deployment of surrogate models.

Industrial prediction and soft sensing often fail due to field data suffering from bias, latency, or seemingly plausible yet unreliable measurements. This work proposes a large language model (LLM)-guided Measurement Credibility Correction (MCC) method that, for the first time, leverages semantic information from process documentation to construct an external reference—requiring neither numerical correlations, fault labels, nor explicit process equations—for lightweight pre-inference correction. MCC translates document semantics into reference signals compatible with numerical models and integrates them at the front end of the prediction pipeline. Evaluated on multiple real-world industrial tasks, MCC reduces average relative MAE by 30.7% on authentic test data and by 80.3% under controlled contamination, while adding only 0.5–2.0k online parameters and incurring a maximum inference latency of 0.089 ms per step.