perform steady-state analysis

Accurately identifying the transition point from warm-up to steady state in performance metric time series is critical for improving benchmark accuracy and reproducibility. This paper introduces, for the first time, steady-state detection principles from chemical process engineering into systems performance analysis. We propose an online step-change point detection method that integrates kernel-based smoothing, sliding-window statistical hypothesis testing, and adaptive thresholding—enhancing robustness against noise and irregular temporal patterns. Compared with the state-of-the-art approaches, our method reduces total error by 14.5% and significantly improves the precision of steady-state onset localization. It effectively mitigates performance evaluation bias caused by premature or delayed steady-state declarations. The framework enables automated, high-fidelity benchmarking, establishing a new paradigm for reliable system performance assessment.

This paper addresses three critical issues in dynamically misspecified state-space models: (i) filter estimates violating model constraints, (ii) inconsistent parameter estimation, and (iii) invalid hypothesis testing. To resolve them, we propose the Sequential Optimal Transport (SOT) framework, which iteratively maps observations to structurally consistent conditional distributions via optimal transport, thereby establishing a novel SOT-based filtering and estimation paradigm. We derive closed-form algorithms for linear processes and introduce a new model specification test statistic grounded in the Wasserstein distance. Theoretically, we prove that the SOT estimator is consistent and asymptotically normal. Empirically, SOT significantly improves filtering consistency, parameter interpretability, and overall model fit on macroeconomic and financial datasets—providing a robust inference foundation for complex structural models such as DSGE and affine term-structure models.

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 work addresses a fundamental limitation in current time series modeling approaches: their general lack of grounding in the underlying dynamical systems, which impedes long-term statistical forecasting, generalization to unseen regimes (e.g., critical transitions), and sample-efficient learning. The paper presents the first systematic argument for the foundational value of a dynamical systems perspective in time series modeling and introduces a novel paradigm—Dynamical System Reconstruction (DSR)—that infers latent dynamical mechanisms directly from observational data. This approach substantially enhances model interpretability, generalization capability, and computational efficiency, enabling reliable long-horizon prediction, theoretical performance bound analysis, and effective modeling under low-data regimes. The framework provides both theoretical foundations and practical pathways toward next-generation foundation models for time series.

Addressing the challenge of high-accuracy, high-efficiency, and interpretable modeling for energy systems (e.g., lithium-ion batteries) under data scarcity, this paper proposes the Model-Integrated Neural Network (MINN)—the first method to directly embed differential-algebraic equation (DAE)-based physical structural priors into a neural network architecture. MINN explicitly encodes DAE constraints and employs a hybrid data–physics joint training paradigm to enable end-to-end learning of system-level physical dynamics. Compared to conventional first-principles models, MINN achieves comparable global output accuracy and local electrochemical behavior prediction using only a minimal amount of training data, while accelerating computation by two orders of magnitude. This work uniquely unifies physical interpretability, numerical fidelity, and computational scalability, establishing a new paradigm for control-oriented modeling of sustainable energy systems.

本文针对具有有限维聚合反馈的度量依赖马尔可夫系统，提出了一种计算平稳均衡的框架，并通过自洽方程和导数估计方法解决了该问题。

This study investigates the stationary distribution and stability of stochastic differential equation systems with multimodal uncertain parameters exhibiting superposition effects, using the nonlinear Rosenzweig–MacArthur predator–prey model as a case study. For the first time, multimodal mixture-distributed parameters are incorporated into the stationary analysis of stochastic dynamical systems. System stability is quantified through the eigenvalue distribution of the Jacobian matrix, and posterior stationary density estimates are obtained via the Monte Carlo method proposed by Hoegele (2026). The results reveal that under multimodal parameter uncertainty, the system exhibits a multimodal stationary distribution, accurately delineating regions of stability. This demonstrates the effectiveness and novelty of the proposed framework for uncertainty quantification in complex ecological dynamics.

This study addresses the finite-horizon budget allocation problem under non-stationary changes in return efficiency by formulating it as a closed-loop economic control problem. The authors employ a receding-horizon model predictive control (MPC) approach to dynamically optimize budget allocation, accounting for execution noise and operational constraints. Through comparison with reactive strategies, the research demonstrates that non-stationarity alone is insufficient for MPC to outperform reactive methods; MPC achieves significant and sustained superiority only when the return efficiency exhibits predictable structures that the model can effectively capture, thereby enabling advantageous intertemporal trade-offs. In contrast, under scenarios of random drift or stationarity, MPC offers no notable performance advantage over reactive approaches.

This work addresses the challenge of high online tuning costs and the absence of simulators in real-world reinforcement learning systems by proposing a method that constructs a calibrated model from offline data to approximate environment dynamics, enabling offline hyperparameter selection. The approach is applied for the first time to a municipal water treatment plant, employing a k-nearest neighbors model with Laplacian distance for nexting prediction. Evaluated on high-dimensional, non-stationary data over annual timescales, the method demonstrates strong scalability and robustness to distributional shifts. Experimental results show that the calibrated model generates realistic long-horizon trajectories, accurately reproduces hyperparameter sensitivity trends, and effectively supports fine-grained tuning of the agent’s learning rate.

Traditional system dynamics models are constrained by fixed state spaces and equilibrium assumptions, limiting their ability to capture open-ended evolution in economic, policy, and technological domains. This work proposes a Stability-Driven Assembly (SDA) framework that leverages stability as the sole driving force to enable endogenous selection through stochastic interactions and differential persistence, without requiring predefined fitness functions or genetic structures. Within this framework, system structure and dynamics co-evolve, and—remarkably—fitness-proportional sampling behavior emerges naturally in a non-equilibrium setting for the first time. By transcending the constraints of equilibrium models on sustained innovation and structural emergence, SDA offers a novel paradigm for modeling economics and policy that supports open-ended evolutionary processes.