pwc-archive/papers-with-abstracts huggingface dataset:
Effects of charging and discharging capabilities on trade-offs between model accuracy and computational efficiency in pumped thermal electricity storage
Authors: Taemin Heo, Ruaridh Macdonald
Date: 2024-11-08 00:00:00
Tasks: Computational Efficiency

The increasing need for energy storage solutions to balance variable renewable energy sources has highlighted the potential of Pumped Thermal Electricity Storage (PTES). In this paper, we investigate the trade-offs between model accuracy and computational efficiency in PTES systems. We evaluate a range of PTES models, from physically detailed to simplified variants, focusing on their non-linear charging and discharging capabilities. Our results show that while detailed models provide the most accurate representation of PTES operation by considering mass flow rate ($\dot{m}$) and state of charge (SoC) dependencies, they come at the cost of increased computational complexity. In contrast, simplified models tend to produce overly optimistic predictions by disregarding capability constraints. Other approximated model variants offer a practical compromise, balancing computational efficiency with acceptable accuracy. In particular, models that disregard $\dot{m}$-dependency and approximate nonlinear SoC-dependency with a piecewise linear function achieve similar accuracy to more detailed models but with significantly faster computation times. Our findings offer guidance to modelers in selecting the appropriate PTES representation for their investment models.
Adaptive Tracking Control with Binary-Valued Output Observations
Authors: Lantian Zhang, Lei Guo
Date: 2024-11-08 00:00:00
Tasks: parameter estimation

This paper considers real-time control and learning problems for finite-dimensional linear systems under binary-valued and randomly disturbed output observations. This has long been regarded as an open problem because the exact values of the traditional regression vectors used in the construction of adaptive algorithms are unavailable, as one only has binary-valued output information. To overcome this difficulty, we consider the adaptive estimation problem of the corresponding infinite-impulse-response (IIR) dynamical systems, and apply the double array martingale theory that has not been previously used in adaptive control. This enables us to establish global convergence results for both the adaptive prediction regret and the parameter estimation error, without resorting to such stringent data conditions as persistent excitation and bounded system signals that have been used in almost all existing related literature. Based on this, an adaptive control law will be designed that can effectively combine adaptive learning and feedback control. Finally, we are able to show that the closed-loop adaptive control system is optimal in the sense that the long-run average tracking error is minimized almost surely for any given bounded reference signals. To the best of the authors' knowledge, this appears to be the first adaptive control result for general linear systems with general binary sensors and arbitrarily given bounded reference signals.
An Adaptive Online Smoother with Closed-Form Solutions and Information-Theoretic Lag Selection for Conditional Gaussian Nonlinear Systems
Authors: Marios Andreou, Nan Chen, Yingda Li
Date: 2024-11-07 00:00:00
Tasks: Form, parameter estimation, State Estimation

Data assimilation (DA) combines partial observations with a dynamical model to improve state estimation. Filter-based DA uses only past and present data and is the prerequisite for real-time forecasts. Smoother-based DA exploits both past and future observations. It aims to fill in missing data, provide more accurate estimations, and develop high-quality datasets. However, the standard smoothing procedure requires using all historical state estimations, which is storage-demanding, especially for high-dimensional systems. This paper develops an adaptive-lag online smoother for a large class of complex dynamical systems with strong nonlinear and non-Gaussian features, which has important applications to many real-world problems. The adaptive lag allows the DA to utilize only observations within a nearby window, significantly reducing computational storage. Online lag adjustment is essential for tackling turbulent systems, where temporal autocorrelation varies significantly over time due to intermittency, extreme events, and nonlinearity. Based on the uncertainty reduction in the estimated state, an information criterion is developed to systematically determine the adaptive lag. Notably, the mathematical structure of these systems facilitates the use of closed analytic formulae to calculate the online smoother and the adaptive lag, avoiding empirical tunings as in ensemble-based DA methods. The adaptive online smoother is applied to studying three important scientific problems. First, it helps detect online causal relationships between state variables. Second, its advantage of computational storage is illustrated via Lagrangian DA, a high-dimensional nonlinear problem. Finally, the adaptive smoother advances online parameter estimation with partial observations, emphasizing the role of the observed extreme events in accelerating convergence.
Modeling Nonlinear Oscillator Networks Using Physics-Informed Hybrid Reservoir Computing
Authors: Andrew Shannon, Conor Houghton, David Barton, Martin Homer
Date: 2024-11-07 00:00:00

Surrogate modeling of non-linear oscillator networks remains challenging due to discrepancies between simplified analytical models and real-world complexity. To bridge this gap, we investigate hybrid reservoir computing, combining reservoir computing with "expert" analytical models. Simulating the absence of an exact model, we first test the surrogate models with parameter errors in their expert model. Second, in a residual physics task, we assess their performance when their expert model lacks key non-linear coupling terms present in an extended ground-truth model. We focus on short-term forecasting across diverse dynamical regimes, evaluating the use of these surrogates for control applications. We show that hybrid reservoir computers generally outperform standard reservoir computers and exhibit greater robustness to parameter tuning. This advantage is less pronounced in the residual physics task. Notably, unlike standard reservoir computers, the performance of the hybrid does not degrade when crossing an observed spectral radius threshold. Furthermore, there is good performance for dynamical regimes not accessible to the expert model, demonstrating the contribution of the reservoir.
Mitigating Stop-and-Go Traffic Congestion with Operator Learning
Authors: Yihuai Zhang, Ruiguo Zhong, Huan Yu
Date: 2024-11-07 00:00:00
Tasks: Computational Efficiency, Operator learning

This paper presents a novel neural operator learning framework for designing boundary control to mitigate stop-and-go congestion on freeways. The freeway traffic dynamics are described by second-order coupled hyperbolic partial differential equations (PDEs). The proposed framework learns feedback boundary control strategies from the closed-loop PDE solution using backstepping controllers, which are widely employed for boundary stabilization of PDE systems. The PDE backstepping control design is time-consuming and requires intensive depth of expertise, since it involves constructing and solving backstepping control kernels. To address these challenges, we present neural operator (NO) learning schemes for the ARZ traffic system that not only ensure closed-loop stability robust to parameter and initial condition variations but also accelerate boundary controller computation. The stability guarantee of the NO-approximated control laws is obtained using Lyapunov analysis. We further propose the physics-informed neural operator (PINO) to reduce the reliance on extensive training data. The performance of the NO schemes is evaluated by simulated and real traffic data, compared with the benchmark backstepping controller, a Proportional Integral (PI) controller, and a PINN-based controller. The NO-approximated methods achieve a computational speedup of approximately 300 times with only a 1% error trade-off compared to the backstepping controller, while outperforming the other two controllers in both accuracy and computational efficiency. The robustness of the NO schemes is validated using real traffic data, and tested across various initial traffic conditions and demand scenarios. The results show that neural operators can significantly expedite and simplify the process of obtaining controllers for traffic PDE systems with great potential application for traffic management.
Efficient and Robust Freeway Traffic Speed Estimation under Oblique Grid using Vehicle Trajectory Data
Authors: Yang He, Chengchuan An, Yuheng Jia, Jiachao Liu, Zhenbo Lu, Jingxin Xia
Date: 2024-11-06 00:00:00
Tasks: Low-Rank Matrix Completion, Matrix Completion, State Estimation

Accurately estimating spatiotemporal traffic states on freeways is a significant challenge due to limited sensor deployment and potential data corruption. In this study, we propose an efficient and robust low-rank model for precise spatiotemporal traffic speed state estimation (TSE) using lowpenetration vehicle trajectory data. Leveraging traffic wave priors, an oblique grid-based matrix is first designed to transform the inherent dependencies of spatiotemporal traffic states into the algebraic low-rankness of a matrix. Then, with the enhanced traffic state low-rankness in the oblique matrix, a low-rank matrix completion method is tailored to explicitly capture spatiotemporal traffic propagation characteristics and precisely reconstruct traffic states. In addition, an anomaly-tolerant module based on a sparse matrix is developed to accommodate corrupted data input and thereby improve the TSE model robustness. Notably, driven by the understanding of traffic waves, the computational complexity of the proposed efficient method is only correlated with the problem size itself, not with dataset size and hyperparameter selection prevalent in existing studies. Extensive experiments demonstrate the effectiveness, robustness, and efficiency of the proposed model. The performance of the proposed method achieves up to a 12% improvement in Root Mean Squared Error (RMSE) in the TSE scenarios and an 18% improvement in RMSE in the robust TSE scenarios, and it runs more than 20 times faster than the state-of-the-art (SOTA) methods.
Improved Convolution-Based Analysis for Worst-Case Probability Response Time of CAN
Authors: Haozhe Yi, Junyi Liu, Maolin Yang, Zewei Chen, Xu Jiang
Date: 2024-11-06 00:00:00

Controller Area Networks (CANs) are widely adopted in real-time automotive control and are increasingly standard in factory automation. Considering their critical application in safety-critical systems, The error rate of the system must be accurately predicted and guaranteed. Through simulation, it is possible to obtain a low-precision overview of the system's behavior. However, for low-probability events, the required number of samples in simulation increases rapidly, making it difficult to conduct a sufficient number of simulations in practical applications, and the statistical results may deviate from the actual outcomes. Therefore, a formal analysis is needed to evaluate the error rate of the system. This paper improves the worst-case probability response time analysis by using convolution-based busy-window and backlog techniques under the error retransmission protocol of CANs. Empirical analysis shows that the proposed method improves upon existing methods in terms of accuracy and efficiency.
cgNA+min: computation of sequence-dependent dsDNA energy-minimising minicircle configurations
Authors: Raushan Singh, Jaroslaw Glowacki, Marius Beaud, Federica Padovano, Robert S. Manning, John H. Maddocks
Date: 2024-11-09 00:00:00

DNA minicircles are closed double-stranded DNA (dsDNA) fragments that have been demonstrated to be an important experimental tool to understand supercoiled, or stressed, DNA mechanics, such as nucleosome positioning and DNA-protein interactions. Specific minicircles can be simulated using Molecular Dynamics (MD) simulation. However, the enormous sequence space makes it unfeasible to exhaustively explore the sequence-dependent mechanics of DNA minicircles using either experiment or MD. For linear fragments, the cgNA+ model, a computationally efficient sequence-dependent coarse-grained model using enhanced Curves+ internal coordinates (rigid base plus rigid phosphate) of double-stranded nucleic acids (dsNAs), predicts highly accurate nonlocal sequence-dependent equilibrium distributions for an arbitrary sequence when compared with MD simulations. This article addresses the problem of modeling sequence-dependent topologically closed and, therefore, stressed fragments of dsDNA. We introduce cgNA+min, a computational approach within the cgNA+ framework, which extends the cgNA+ model applicability to compute the sequence-dependent energy minimising configurations of covalently closed dsDNA minicircles of various lengths and linking numbers (Lk). The main idea is to derive the appropriate chain rule to express the cgNA+ energy in absolute coordinates involving quaternions where the closure condition is simple to handle. We also present a semi-analytic method for efficiently computing sequence-dependent initial minicircles having arbitrary Lk and length. For different classes and lengths of sequences, we demonstrate that the dsDNA minicircle energies computed using cgNA+min agree well with the energies approximated from experimentally measured J-factor values. Finally, we present the minicircle shape, energy, and multiplicity of Lk for more than 120K random DNA sequences of different lengths.
Input-Driven Dynamics for Robust Memory Retrieval in Hopfield Networks
Authors: Simone Betteti, Giacomo Baggio, Francesco Bullo, Sandro Zampieri
Date: 2024-11-06 00:00:00
Tasks: Retrieval

The Hopfield model provides a mathematically idealized yet insightful framework for understanding the mechanisms of memory storage and retrieval in the human brain. This model has inspired four decades of extensive research on learning and retrieval dynamics, capacity estimates, and sequential transitions among memories. Notably, the role and impact of external inputs has been largely underexplored, from their effects on neural dynamics to how they facilitate effective memory retrieval. To bridge this gap, we propose a novel dynamical system framework in which the external input directly influences the neural synapses and shapes the energy landscape of the Hopfield model. This plasticity-based mechanism provides a clear energetic interpretation of the memory retrieval process and proves effective at correctly classifying highly mixed inputs. Furthermore, we integrate this model within the framework of modern Hopfield architectures, using this connection to elucidate how current and past information are combined during the retrieval process. Finally, we embed both the classic and the new model in an environment disrupted by noise and compare their robustness during memory retrieval.
Hierarchical genotype networks and incipient ecological speciation in Q$β$ phage quasispecies
Authors: Luis F Seoane, Henry Secaira-Morocho, Ester Lázaro, Susanna Manrubia
Date: 2024-11-11 00:00:00

Understanding how viral mutant spectra organize and explore genotype space is essential for unraveling the mechanisms driving evolution at the finest scale. Here we use deep-sequencing data of an amplicon in the A2 protein of the RNA bacteriophage Q$\beta$ to reconstruct genotype networks with tens of thousands of different haplotypes. The study of populations evolved under different temperature regimes uncovers generic topological features conditioned by fundamental structural motifs of genotype networks -- tetrahedrons, triangles, and squares -- that govern their local architecture. Mutant swarms display a hierarchical structure where sequences cluster around a highly connected and abundant sequence core that sustains population diversity. The immediate neighborhood of this core is comprehensively sampled, with no signs of selection, while a few mutations away sampling becomes dynamical and sparse, showing signs of purifying selection. By aggregating genotype networks from populations adapted to different temperatures, we capture the early stages of evolutionary divergence, with overlapping populations that remain connected through short mutational paths. Even at the time scale of these experiments, evolutionary pathways might be multiple, preventing the backward reconstruction of unique trajectories once mutations have been fixed. This analysis provides a detailed view of the local, fine-scale processes shaping viral quasispecies evolution and underscores the usefulness of genotype networks as an enlightening visualization of the organization of mutant swarms.
Human game experiment to verify the equilibrium selection controlled by design
Authors: Wang Zhijian, Shan Lixia, Yao Qinmei, Wang Yijia
Date: 2024-11-11 00:00:00

We conducted a laboratory experiment involving human subjects to test the theoretical hypothesis that equilibrium selection can be impacted by manipulating the games dynamics process, by using modern control theory. Our findings indicate that human behavior consists with the predictions derived from evolutionary game theory paradigm. The consistency is supported by three key observations: (1) the long-term distribution of strategies in the strategy space, (2) the cyclic patterns observed within this space, and (3) the speed of convergence to the selected equilibrium. These findings suggest that the design of controllers aimed at equilibrium selection can indeed achieve their theoretical intended purpose. The location of this study in the knowledge tree of evolutionary game science is presented.
Events in Noise-Driven Oscillators: Markov Renewal Processes and the "Unruly" Breakdown of Phase-Reduction Theory
Authors: Avinash J. Karamchandani
Date: 2024-10-24 00:00:00

We introduce an extension to the standard reduction of oscillatory systems to a single phase variable. The standard reduction is often insufficient, particularly when the oscillations have variable amplitude and the magnitude of each oscillatory excursion plays a defining role in the impact of that oscillator on other systems, i.e. on its output. For instance, large excursions in bursting or mixed-mode neural oscillators may constitute events like action potentials, which trigger output to other neurons, while smaller, sub-threshold oscillations generate no output and therefore induce no coupling between neurons. Noise induces diffusion-like dynamics of the oscillator phase on top of its otherwise constant rate-of-change, resulting in the irregular occurrence of these output events. We model the events as corresponding to distinguished crossings of a Poincare section. Using a linearization of the noisy Poincare map and its description under phase-isostable coordinates, we determine the diffusion coefficient for the occurrence and timing of the events using Markov renewal theory. We show that for many oscillator models the corresponding point process can exhibit "unruly" diffusion: with increasing input noise strength the diffusion coefficient vastly increases compared to the standard phase reduction analysis, and, strikingly, it also decreases when the input noise strength is increased further. We provide a thorough analysis in the case of planar oscillators, which exhibit unruliness in a finite region of the natural parameter space. Our results in part explain the surprising synchronization behavior obtained in pulse-coupled, mixed-mode oscillators as they arise, e.g., in neural systems.
Community Research Earth Digital Intelligence Twin (CREDIT)
Authors: John Schreck, Yingkai Sha, William Chapman, Dhamma Kimpara, Judith Berner, Seth McGinnis, Arnold Kazadi, Negin Sobhani, Ben Kirk, David John Gagne II
Date: 2024-11-09 00:00:00

Recent advancements in artificial intelligence (AI) for numerical weather prediction (NWP) have significantly transformed atmospheric modeling. AI NWP models outperform traditional physics-based systems, such as the Integrated Forecast System (IFS), across several global metrics while requiring fewer computational resources. However, existing AI NWP models face limitations related to training datasets and timestep choices, often resulting in artifacts that reduce model performance. To address these challenges, we introduce the Community Research Earth Digital Intelligence Twin (CREDIT) framework, developed at NSF NCAR. CREDIT provides a flexible, scalable, and user-friendly platform for training and deploying AI-based atmospheric models on high-performance computing systems. It offers an end-to-end pipeline for data preprocessing, model training, and evaluation, democratizing access to advanced AI NWP capabilities. We demonstrate CREDIT's potential through WXFormer, a novel deterministic vision transformer designed to predict atmospheric states autoregressively, addressing common AI NWP issues like compounding error growth with techniques such as spectral normalization, padding, and multi-step training. Additionally, to illustrate CREDIT's flexibility and state-of-the-art model comparisons, we train the FUXI architecture within this framework. Our findings show that both FUXI and WXFormer, trained on six-hourly ERA5 hybrid sigma-pressure levels, generally outperform IFS HRES in 10-day forecasts, offering potential improvements in efficiency and forecast accuracy. CREDIT's modular design enables researchers to explore various models, datasets, and training configurations, fostering innovation within the scientific community.
Analysis, forecasting and system identification of a floating offshore wind turbine using dynamic mode decomposition
Authors: Giorgio Palma, Andrea Bardazzi, Alessia Lucarelli, Chiara Pilloton, Andrea Serani, Claudio Lugni, Matteo Diez
Date: 2024-11-08 00:00:00
Tasks: Uncertainty Quantification

This article presents the data-driven equation-free modeling of the dynamics of a hexafloat floating offshore wind turbine based on the application of dynamic mode decomposition (DMD). All the analyses are performed on experimental data collected from an operating prototype. The DMD has here used i) to extract knowledge from the dynamic system through its modal analysis, ii) for short-term forecasting from the knowledge of the immediate past of the system state, and iii) for the system identification and reduced order modeling. The forecasting method for the motions, accelerations, and forces acting on the floating system is developed using Hankel-DMD, a methodological extension that includes time-delayed copies of the states in an augmented state vector. The system identification task is performed by applying Hankel-DMD with control (Hankel-DMDc), which models the system including the effect of forcing terms. The influence of the main hyperparameters of the methods, namely the number of delayed copies in the state and input vector and the length of the observation time, is investigated with a full factorial analysis using three error metrics analyzing complementary aspects of the prediction: the normalized root mean square error, the normalized average minimum-maximum absolute error, and the Jensen-Shannon divergence. A Bayesian extension of the Hankel-DMD and Hankel-DMDc is introduced by considering the hyperparameters as stochastic variables varying in suitable ranges defined after the full factorial analysis, enriching the predictions with uncertainty quantification. Results show the capability of the approaches for short-term forecasting and system identification, suggesting their potential for real-time continuously-learning digital twinning and surrogate data-driven reduced order modeling.