pwc-archive/papers-with-abstracts huggingface dataset:
Physics-informed Neural Networks for Heterogeneous Poroelastic Media
Authors: Sumanta Roy, Chandrasekhar Annavarapu, Pratanu Roy, Dakshina Murthy Valiveti
Date: 2024-07-01 00:00:00

This study presents a novel physics-informed neural network (PINN) framework for modeling poroelasticity in heterogeneous media with material interfaces. The approach introduces a composite neural network (CoNN) where separate neural networks predict displacement and pressure variables for each material. While sharing identical activation functions, these networks are independently trained for all other parameters. To address challenges posed by heterogeneous material interfaces, the CoNN is integrated with the Interface-PINNs or I-PINNs framework (Sarma et al. 2024, https://dx.doi.org/10.1016/j.cma.2024.117135), allowing different activation functions across material interfaces. This ensures accurate approximation of discontinuous solution fields and gradients. Performance and accuracy of this combined architecture were evaluated against the conventional PINNs approach, a single neural network (SNN) architecture, and the eXtended PINNs (XPINNs) framework through two one-dimensional benchmark examples with discontinuous material properties. The results show that the proposed CoNN with I-PINNs architecture achieves an RMSE that is two orders of magnitude better than the conventional PINNs approach and is at least 40 times faster than the SNN framework. Compared to XPINNs, the proposed method achieves an RMSE at least one order of magnitude better and is 40% faster.
Learning Li-ion battery health and degradation modes from data with aging-aware circuit models
Authors: ZiHao Zhou, Antti Aitio, David Howey
Date: 2024-07-09 00:00:00

Non-invasive estimation of Li-ion battery state-of-health from operational data is valuable for battery applications, but remains challenging. Pure model-based methods may suffer from inaccuracy and long-term instability of parameter estimates, whereas pure data-driven methods rely heavily on training data quality and quantity, causing lack of generality when extrapolating to unseen cases. We apply an aging-aware equivalent circuit model for health estimation, combining the flexibility of data-driven techniques within a model-based approach. A simplified electrical model with voltage source and resistor incorporates Gaussian process regression to learn capacity fade over time and also the dependence of resistance on operating conditions and time. The approach was validated against two datasets and shown to give accurate performance with less than 1% relative root mean square error (RMSE) in capacity and less than 2% mean absolute percentage error (MAPE). Critically, we show that the open circuit voltage versus state-of-charge function must be accurately known, and any inaccuracies or changes in this over time strongly influence the inferred resistance. However, this feature (or bug) may also be used to estimate in operando differential voltage curves from operational data.
Data-Driven Multi-Modal Learning Model Predictive Control
Authors: Fionna B. Kopp, Francesco Borrelli
Date: 2024-07-08 00:00:00
Tasks: Friction, Model Predictive Control

We present a Learning Model Predictive Controller (LMPC) for multi-modal systems performing iterative control tasks. Assuming availability of historical data, our goal is to design a data-driven control policy for the multi-modal system where the current mode is unknown. First, we propose a novel method to select local data for constructing affine time-varying (ATV) models of a multi-modal system in the context of LMPC. Then we present how to build a sampled safe set from multi-modal historical data. We demonstrate the effectiveness of our method through simulation results of automated driving on a friction-varying track.
Time Series Dataset for Modeling and Forecasting of $N_2O$ in Wastewater Treatment
Authors: Laura Debel Hansen, Anju Rani, Mikkel Algren Stokholm-Bjerregaard, Peter Alexander Stentoft, Daniel Ortiz Arroyo, Petar Durdevic
Date: 2024-07-08 00:00:00
Tasks: Time Series, Time Series Forecasting

In this paper, we present two years of high-resolution nitrous oxide ($N_2O$) measurements for time series modeling and forecasting in wastewater treatment plants (WWTP). The dataset comprises frequent, real-time measurements from a full-scale WWTP, with a sample interval of 2 minutes, making it ideal for developing models for real-time operation and control. This comprehensive bio-chemical dataset includes detailed influent and effluent parameters, operational conditions, and environmental factors. Unlike existing datasets, it addresses the unique challenges of modeling $N_2O$, a potent greenhouse gas, providing a valuable resource for researchers to enhance predictive accuracy and control strategies in wastewater treatment processes. Additionally, this dataset significantly contributes to the fields of machine learning and deep learning time series forecasting by serving as a benchmark that mirrors the complexities of real-world processes, thus facilitating advancements in these domains. We provide a detailed description of the dataset along with a statistical analysis to highlight its characteristics, such as nonstationarity, nonnormality, seasonality, heteroscedasticity, structural breaks, asymmetric distributions, and intermittency, which are common in many real-world time series datasets and pose challenges for forecasting models.
Arbitrary Waveform Generated Metasurface: A New Paradigm for Direct Modulation and Beamforming Decoupling
Authors: Xuehui Dong, Bokai Lai, Rujing Xiong, Jianan Zhang, Miyu Feng, Tiebin Mi, Robert Caiming Qiu
Date: 2024-07-05 00:00:00

Information Metasurface, also known as reconfigurable intelligent surface (RIS) has gained significant attention owing to its impressive abilities in electromagnetic (EM) wave manipulation with simple structures. Numerous studies focus on achieving efficient and versatile information transmission using RIS across various fields like wireless communication, radar detection, integrated sensing, and communications, among others. Previous studies demonstrate diverse approaches to achieve reflection modulation by utilizing the superposition of the quantified reflection coefficient (RC) of each unit but suffer from the computing complexity of codebook sequence, the safety of communication, and the flexibility of modulation. To address these challenges, we introduce a novel concept of information metasurface, namely AWG-RIS, which is capable of independently producing arbitrary baseband waveforms and beam patterns through a design that decouples magnitude and phase, without changing the beam pattern. The AWG-RIS functions as a reflection mixer, directly embedding the intended signal into the incoming EM waves. Subsequently, we developed an analysis framework and introduced the waveform factor and beamforming factor into the new model, offering theoretical support for the transition from the control signal to the outgoing electromagnetic wave. Additionally, we unveil the world's first prototype showcasing passive arbitrary waveform generation while maintaining the beam pattern unaltered. Leveraging the decoupling of direct modulation and beamforming, we explore additional applications in several domains relative to traditional RISs. Finally, we present experiments that confirm the generation of arbitrary waveforms and particular spectrograms.
Collaborative Secret and Covert Communications for Multi-User Multi-Antenna Uplink UAV Systems: Design and Optimization
Authors: Jinpeng Xu, Lin Bai, Xin Xie, Lin Zhou
Date: 2024-07-08 00:00:00

Motivated by diverse secure requirements of multi-user in UAV systems, we propose a collaborative secret and covert transmission method for multi-antenna ground users to unmanned aerial vehicle (UAV) communications. Specifically, based on the power domain non-orthogonal multiple access (NOMA), two ground users with distinct security requirements, named Bob and Carlo, superimpose their signals and transmit the combined signal to the UAV named Alice. An adversary Willie attempts to simultaneously eavesdrop Bob's confidential message and detect whether Carlo is transmitting or not. We derive close-form expressions of the secrecy connection probability (SCP) and the covert connection probability (CCP) to evaluate the link reliability for wiretap and covert transmissions, respectively. Furthermore, we bound the secrecy outage probability (SOP) from Bob to Alice and the detection error probability (DEP) of Willie to evaluate the link security for wiretap and covert transmissions, respectively. To characterize the theoretical benchmark of the above model, we formulate a weighted multi-objective optimization problem to maximize the average of secret and covert transmission rates subject to constraints SOP, DEP, the beamformers of Bob and Carlo, and UAV trajectory parameters. To solve the optimization problem, we propose an iterative optimization algorithm using successive convex approximation and block coordinate descent (SCA-BCD) methods. Our results reveal the influence of design parameters of the system on the wiretap and covert rates, analytically and numerically. In summary, our study fills the gaps in joint secret and covert transmission for multi-user multi-antenna uplink UAV communications and provides insights to construct such systems.
Towards Reliable Neural Optimizers: A Permutation Equivariant Neural Approximation for Information Processing Applications
Authors: Meiyi Li, Javad Mohammadi
Date: 2024-07-07 00:00:00
Tasks: Decision Making

The complexities of information processing across Dynamic Data Driven Applications Systems drive the development and adoption of Artificial Intelligence-based optimization solutions. Traditional solvers often suffer from slow response times and an inability to adapt swiftly to real-time input variations. To address these deficiencies, we will expand on our previous research in neural-based optimizers by introducing a machine learning-enabled neural approximation model called LOOP-PE (Learning to Optimize the Optimization Process -- Permutation Equivariance version). This model not only enhances decision-making efficiency but also dynamically adapts to variations of data collections from sensor networks. In this work, we focus on mitigating the heterogeneity issues of data collection from sensor networks, including sensor dropout and failures, communication delays, and the complexities involved in integrating new sensors during system scaling. The proposed LOOP-PE model specifically overcomes these issues with a unique structure that is permutation equivariant, allowing it to accommodate inputs from a varying number of sensors and directly linking these inputs to their optimal operational outputs. This design significantly boosts the system's flexibility and adaptability, especially in scenarios characterized by unordered, distributed, and asynchronous data collections. Moreover, our approach increases the robustness of decision-making by integrating physical constraints through the generalized gauge map method, which theoretically ensures the decisions' practical feasibility and operational viability under dynamic conditions. We use a DDDAS case study to demonstrate that LOOP-PE model reliably delivers near-optimal and adaptable solutions, significantly outperforming traditional methods in managing the complexities of multi-sensor environments for real-time deployments.
Channel Characterization of IRS-assisted Resonant Beam Communication Systems
Authors: Wen Fang, Wen Chen, Qingqing Wu, Xusheng Zhu, Qiong Wu, Nan Cheng
Date: 2024-07-07 00:00:00

To meet the growing demand for data traffic, spectrum-rich optical wireless communication (OWC) has emerged as a key technological driver for the development of 6G. The resonant beam communication (RBC) system, which employs spatially separated laser cavities as the transmitter and receiver, is a high-speed OWC technology capable of self-alignment without tracking. However, its transmission through the air is susceptible to losses caused by obstructions. In this paper, we propose an intelligent reflecting surface (IRS) assisted RBC system with the optical frequency doubling method, where the resonant beam in frequency-fundamental and frequency-doubled is transmitted through both direct line-of-sight (LoS) and IRS-assisted channels to maintain steady-state oscillation and enable communication without echo-interference, respectively. Then, we establish the channel model based on Fresnel diffraction theory under the near-field optical propagation to analyze the transmission loss and frequency-doubled power analytically. Furthermore, communication power can be maximized by dynamically controlling the beam-splitting ratio between the two channels according to the loss levels encountered over air. Numerical results validate that the IRS-assisted channel can compensate for the losses in the obstructed LoS channel and misaligned receivers, ensuring that communication performance reaches an optimal value with dynamic ratio adjustments.
Deception in Nash Equilibrium Seeking
Authors: Michael Tang, Umar Javed, Xudong Chen, Miroslav Krstic, Jorge I. Poveda
Date: 2024-07-06 00:00:00

In socio-technical multi-agent systems, deception exploits privileged information to induce false beliefs in "victims," keeping them oblivious and leading to outcomes detrimental to them or advantageous to the deceiver. We consider model-free Nash-equilibrium-seeking for non-cooperative games with asymmetric information and introduce model-free deceptive algorithms with stability guarantees. In the simplest algorithm, the deceiver includes in his action policy the victim's exploration signal, with an amplitude tuned by an integrator of the regulation error between the deceiver's actual and desired payoff. The integral feedback drives the deceiver's payoff to the payoff's reference value, while the victim is led to adopt a suboptimal action, at which the pseudogradient of the deceiver's payoff is zero. The deceiver's and victim's actions turn out to constitute a "deceptive" Nash equilibrium of a different game, whose structure is managed - in real time - by the deceiver. We examine quadratic, aggregative, and more general games and provide conditions for a successful deception, mutual and benevolent deception, and immunity to deception. Stability results are established using techniques based on averaging and singular perturbations. Among the examples in the paper is a microeconomic duopoly in which the deceiver induces in the victim a belief that the buyers disfavor the deceiver more than they actually do, leading the victim to increase the price above the Nash price, and resulting in an increased profit for the deceiver and a decreased profit for the victim. A study of the deceiver's integral feedback for the desired profit reveals that, in duopolies with equal marginal costs, a deceiver that is greedy for very high profit can attain any such profit, and pursue this with arbitrarily high integral gain (impatiently), irrespective of the market preference for the victim.
Data-driven model predictive control of battery storage units
Authors: Johannes B. Lipka, Christian A. Hans
Date: 2024-07-06 00:00:00
Tasks: Model Predictive Control

In many state-of-the-art control approaches for power systems with storage units, an explicit model of the storage dynamics is required. With growing numbers of storage units, identifying these dynamics can be cumbersome. This paper employs recent data-driven control approaches that do not require an explicit identification step. Instead, they use measured input/output data in control formulations. In detail, we propose an economic data-driven model predictive control (MPC) scheme to operate a small power system with input-nonlinear battery dynamics. First, a linear data-driven MPC approach that uses a slack variable to account for plant-model-mismatch is proposed. In a second step, an input-nonlinear data-driven MPC scheme is deduced. Comparisons with a reference indicate that the linear data-driven MPC approximates the nonlinear plant in an acceptable manner. Even better results, however, can be obtained with the input-nonlinear data-driven MPC scheme which provides increased prediction accuracy.
Collaborative Estimation of Real Valued Function by Two Agents and a Fusion Center with Knowledge Exchange
Authors: Aneesh Raghavan, Karl H. Johansson
Date: 2024-07-06 00:00:00
Tasks: valid

We consider a collaborative iterative algorithm with two agents and a fusion center for estimation of a real valued function (or ``model") on the set of real numbers. While the data collected by the agents is private, in every iteration of the algorithm, the models estimated by the agents are uploaded to the fusion center, fused, and, subsequently downloaded by the agents. We consider the estimation spaces at the agents and the fusion center to be Reproducing Kernel Hilbert Spaces (RKHS). Under suitable assumptions on these spaces, we prove that the algorithm is consistent, i.e., there exists a subsequence of the estimated models which converges to a model in the strong topology. To this end, we define estimation operators for the agents, fusion center, and, for every iteration of the algorithm constructively. We define valid input data sequences, study the asymptotic properties of the norm of the estimation operators, and, find sufficient conditions under which the estimation operator until any iteration is uniformly bounded. Using these results, we prove the existence of an estimation operator for the algorithm which implies the consistency of the considered estimation algorithm.
Control Density Function for Robust Safety and Convergence
Authors: Joseph Moyalan, Sriram S. K. S Narayanan, Umesh Vaidya
Date: 2024-07-06 00:00:00

We introduce a novel approach for safe control design based on the density function. A control density function (CDF) is introduced to synthesize a safe controller for a nonlinear dynamic system. The CDF can be viewed as a dual to the control barrier function (CBF), a popular approach used for safe control design. While the safety certificate using the barrier function is based on the notion of invariance, the dual certificate involving the density function has a physical interpretation of occupancy. This occupancy-based physical interpretation is instrumental in providing an analytical construction of density function used for safe control synthesis. The safe control design problem is formulated using the density function as a quadratic programming (QP) problem. In contrast to the QP proposed for control synthesis using CBF, the proposed CDF-based QP can combine both the safety and convergence conditions to target state into single constraints. Further, we consider robustness against uncertainty in system dynamics and the initial condition and provide theoretical results for robust navigation using the CDF. Finally, we present simulation results for safe navigation with single integrator and double-gyre fluid flow-field examples, followed by robust navigation using the bicycle model and autonomous lane-keeping examples.
A game theory analysis of decentralized epidemic management with opinion dynamics
Authors: Olivier Lindamulage De Silva, Samson Lasaulce, Irinel-Constantin Morarescu, Vineeth S. Varma
Date: 2024-07-06 00:00:00
Tasks: Management

In this paper, we introduce a static game that allows one to numerically assess the loss of efficiency induced by decentralized control or management of a global epidemic. Each player represents a region which is assumed to choose its control to implement a tradeoff between socio-economic aspects and health aspects; the control comprises both epidemic control physical measures and influence actions on the region opinion. The Generalized Nash equilibrium $(\mathrm{GNE})$ analysis of the proposed game model is conducted. The direct analysis of this game of practical interest is non-trivial but it turns out that one can construct an auxiliary game which allows one: to prove existence and uniqueness; to compute the GNE and the optimal centralized solution (sum-cost) of the game. These results allow us to assess numerically the loss (measured in terms of Price of Anarchy ($\mathrm{PoA}$)) induced by decentralization with or without taking into account the opinion dynamics.
On the Scientific Method: The Role of Hypotheses and Involved Mathematics
Authors: Mario Milanese, Carlo Novara, Michele Taragna
Date: 2024-07-06 00:00:00

The paper investigates the role of data, hypotheses and mathematical methods that can be used in the discovery of a law y=fo(u), relating variables u and y of a physical phenomenon, making use of experimental measurements of such variables. Since the exact knowledge of the function fo cannot be expected, the problem of deriving approximate functions giving a small approximation error, measured by some function norm, is discussed. The main contributions of the paper are summarized as follows. At first, it is proven that deriving a reliable approximation, i.e., having a finite error, is not possible using measured data only. Thus, for deriving a reliable approximation, hypotheses on the function fo and on the disturbances corrupting the measurements must be introduced. Second, necessary and sufficient conditions for deriving a reliable approximation are provided. If such conditions are satisfied, suitable accuracy properties of the approximation can be defined, called theoretical properties. Third, it is shown that it is not possible to verify the conditions necessary for deriving a reliable approximation, but it is possible to verify that hypotheses on fo and on the disturbances are falsified by experimental measurements, showing that no function and disturbances satisfying the given hypotheses exist, able to reproduce the measurements (this is called falsification property). The above properties are then discussed for hypotheses belonging to the following classes: Parametric Probabilistic, where fo is assumed to be a function depending on a vector p and the disturbances are assumed to be stochastic variables; Set Membership class, where fo is assumed to be a bounded smooth function and the disturbances are assumed to be bounded variables; Parametric Set Membership class, able to integrate Parametric Probabilistic hypotheses with Set Membership hypotheses.
Analysis of Dead Reckoning Accuracy in Swarm Robotics System
Authors: Weihang Tan, Timothy Anglea, Yongqiang Wang
Date: 2024-07-05 00:00:00
Tasks: Position

The objective of this paper is to determine the position of a single mobile robot in a swarm using dead reckoning techniques. We investigate the accuracy of navigation by using this process. The paper begins with the research background and social importance. Then, the specific experimental setup and analysis of experimental results are presented. Finally, the results are detailed and some potential improvements are provided.