pwc-archive/papers-with-abstracts huggingface dataset:
Exergy Battery Modeling and P2P Trading Based Optimal Operation of Virtual Energy Station
Authors: Meng Song, Xinyi Jing, Jianyong Ding, Ciwei Gao, Mingyu Yan, Wensheng Luo, Mariusz Malinowski
Date: 2025-03-15 00:00:00
Tasks: Bilevel Optimization, Scheduling

Virtual energy stations (VESs) work as retailers to provide electricity and natural gas sale services for integrated energy systems (IESs), and guide IESs energy consumption behaviors to tackle the varying market prices via integrated demand response (IDR). However, IES customers are risk averse and show low enthusiasm in responding to the IDR incentive signals. To address this problem, exergy is utilized to unify different energies and allowed to be virtually stored and withdrawn for arbitrage by IESs. The whole incentive mechanism operating process is innovatively characterized by a virtual exergy battery. Peer to peer (P2P) exergy trading based on shared exergy storage is also developed to reduce the energy cost of IESs without any extra transmission fee. In this way, IES can reduce the economic loss risk caused by the market price fluctuation via the different time (time dimension), multiple energy conversion (energy dimension), and P2P exergy trading (space dimension) arbitrage. Moreover, the optimal scheduling of VES and IESs is modeled by a bilevel optimization model. The consensus based alternating direction method of multipliers (CADMM) algorithm is utilized to solve this problem in a distributed way. Simulation results validate the effectiveness of the proposed incentive mechanism and show that the shared exergy storage can enhance the benefits of different type IESs by 18.96%, 3.49%, and 3.15 %, respectively.
Formally Proving Invariant Systemic Properties of Control Programs Using Ghost Code and Integral Quadratic Constraints
Authors: Elias Khalife, Pierre-Loic Garoche, Mazen Farhood
Date: 2025-03-14 00:00:00

This paper focuses on formally verifying invariant properties of control programs both at the model and code levels. The physical process is described by an uncertain discrete-time state-space system, where the dependence of the state-space matrix-valued functions defining the system on the uncertainties can be rational. The proposed approaches make use of pointwise integral quadratic constraints (IQCs) to characterize the uncertainties affecting the behavior of the system. Various uncertainties can be characterized by pointwise IQCs, including static linear time-varying perturbations and sector-bounded nonlinearities. Using the IQC framework, a sound overapproximation of the uncertain system, which is expressible at the code level, is constructed. Tools such as Frama-C, ACSL, WP, and an Alt-Ergo plugin are employed to ensure the validity of the state and output invariant properties across both real and float models. The first proposed approach can be used to formally verify (local) invariant properties of the control code. This capability is demonstrated in a couple of examples involving gain-scheduled path-following controllers designed for an uncrewed aircraft system and an autonomous underwater vehicle. The second approach enables the verification of closed-loop invariant properties, i.e., invariant properties of the controlled system as a whole, in both real and float models, while preserving the integrity of the executable controller code. This is achieved by using ghost code attached to the control code for all elements related to the plant model with uncertainties, as the ghost code does not interfere with the executable code. The effectiveness of this approach is demonstrated in two examples on the control of a four-thruster hovercraft and the control of a two-mass rotational system.
Ignition Point Reachability for Aerodynamically-Controlled Reusable Launch Vehicles
Authors: Benjamin Chung, Kazuya Echigo, Behçet Açıkmeşe
Date: 2025-03-14 00:00:00

We describe a successive convex programming (Sequential Convex Programming (SCP)) based approach for estimate the set of points where a 5-degree of freedom (5-DoF) reusable launch vehicle (RLV) returning to a landing site can transition from aerodynamic to propulsive descent. Determining the set of feasible ignition points that a RLV can use and then safely land is important for mission planning and range safety. However, past trajectory optimization approaches for RLVs consider substantially simplified versions of the vehicle dynamics. Furthermore, prior reachability analysis methods either do not extend to the full constraint set needed for an RLV or are too beset by the curse of dimensionality to handle the full 5-DoF dynamics. To solve this problem, we describe an algorithm that approximates the projection of a high dimensional reachable set onto a low dimensional space. Instead of computing all parts of the reachable space, we only calculate reachability in the projected space of interest by using repeated trajectory optimization to sample the reachable polytope in the reduced space. The optimization can take into account initial and terminal constraints as well as state and control constraints. We show that our algorithm is able to compute the projection of a reachable set into a low dimensional space by calculating the feasible ignition points for a two-phase aerodynamic/propulsive RLV landing trajectory, while also demonstrating the aerodynamic divert enabled by our body and fin actuator model.
Robust Model Predictive Control of Fast Lithium-ion Battery Pretreatment for Safe Recycling
Authors: Meng Yuan, Adam Burman, Changfu Zou
Date: 2025-03-14 00:00:00
Tasks: Model Predictive Control

The proper disposal and repurposing of end-of-life electric vehicle batteries are critical for maximizing their environmental benefits. This study introduces a robust model predictive control (MPC) framework designed to optimize the battery discharging process during pre-treatment, ensuring both efficiency and safety. The proposed method explicitly incorporates temperature constraints to prevent overheating and potential hazards. By leveraging a control-oriented equivalent circuit model integrated with thermal dynamics, the MPC algorithm dynamically adjusts the discharging profile to maintain safe operating temperatures. Additionally, the robust controller is designed to account for model mismatches between the nonlinear battery dynamics and the linearized model, ensuring reliable performance under varying conditions. The effectiveness of this approach is demonstrated through simulations comparing the robust MPC method with conventional discharging strategies, including constant current-constant voltage (CC-CV) and constant current-constant temperature (CC-CT) methods. Results indicate that the robust MPC framework significantly reduces discharging time while adhering to safety constraints, offering a promising solution for the recycling and second-life applications of lithium-ion batteries.
Bayes and Biased Estimators Without Hyper-parameter Estimation: Comparable Performance to the Empirical-Bayes-Based Regularized Estimator
Authors: Yue Ju, Bo Wahlberg, Håkan Hjalmarsson
Date: 2025-03-14 00:00:00
Tasks: parameter estimation

Regularized system identification has become a significant complement to more classical system identification. It has been numerically shown that kernel-based regularized estimators often perform better than the maximum likelihood estimator in terms of minimizing mean squared error (MSE). However, regularized estimators often require hyper-parameter estimation. This paper focuses on ridge regression and the regularized estimator by employing the empirical Bayes hyper-parameter estimator. We utilize the excess MSE to quantify the MSE difference between the empirical-Bayes-based regularized estimator and the maximum likelihood estimator for large sample sizes. We then exploit the excess MSE expressions to develop both a family of generalized Bayes estimators and a family of closed-form biased estimators. They have the same excess MSE as the empirical-Bayes-based regularized estimator but eliminate the need for hyper-parameter estimation. Moreover, we conduct numerical simulations to show that the performance of these new estimators is comparable to the empirical-Bayes-based regularized estimator, while computationally, they are more efficient.
Set-based and Dynamical Feedback-augmented Hands-off Control
Authors: Andrei Sperilă, Sorin Olaru, Stéphane Drobot
Date: 2025-03-14 00:00:00

A novel set-theoretical approach to hands-off control is proposed, which focuses on spatial arguments for command limitation, rather than temporal ones. By employing dynamical feedback alongside invariant set-based constraints, actuation is employed only to drive the system's state inside a "hands-off region" of its state-space, where the plant may freely evolve in open-loop configuration. A computationally-efficient procedure with strong theoretical guarantees is devised, and its effectiveness is showcased via an intuitive practical example.
Forecasting Empty Container availability for Vehicle Booking System Application
Authors: Arthur Cartel Foahom Gouabou, Mohammed Al-Kharaz, Faouzi Hakimi, Tarek Khaled, Kenza Amzil
Date: 2025-03-14 00:00:00

Container terminals, pivotal nodes in the network of empty container movement, hold significant potential for enhancing operational efficiency within terminal depots through effective collaboration between transporters and terminal operators. This collaboration is crucial for achieving optimization, leading to streamlined operations and reduced congestion, thereby benefiting both parties. Consequently, there is a pressing need to develop the most suitable forecasting approaches to address this challenge. This study focuses on developing and evaluating a data-driven approach for forecasting empty container availability at container terminal depots within a Vehicle Booking System (VBS) framework. It addresses the gap in research concerning optimizing empty container dwell time and aims to enhance operational efficiencies in container terminal operations. Four forecasting models-Naive, ARIMA, Prophet, and LSTM-are comprehensively analyzed for their predictive capabilities, with LSTM emerging as the top performer due to its ability to capture complex time series patterns. The research underscores the significance of selecting appropriate forecasting techniques tailored to the specific requirements of container terminal operations, contributing to improved operational planning and management in maritime logistics.
Propensity Formation-Containment Control of Fully Heterogeneous Multi-Agent Systems via Online Data-Driven Learning
Authors: Ao Cao, Fuyong Wang, Zhongxin Liu
Date: 2025-03-12 00:00:00

This paper introduces an online data-driven learning scheme designed to address a novel problem in propensity formation and containment control for fully heterogeneous multi-agent systems. Unlike traditional approaches that rely on the eigenvalues of the Laplacian matrix, this problem considers the determination of follower positions based on propensity factors released by leaders. To address the challenge of incomplete utilization of leader information in existing multi-leader control methods, the concept of an influential transit formation leader (ITFL) is introduced. An adaptive observer is developed for the agents, including the ITFL, to estimate the state of the tracking leader or the leader's formation. Building on these observations, a model-based control protocol is proposed, elucidating the relationship between the regulation equations and control gains, ensuring the asymptotic convergence of the agent's state. To eliminate the necessity for model information throughout the control process, a new online data-driven learning algorithm is devised for the control protocol. Finally, numerical simulation results are given to verify the effectiveness of the proposed method.
Balancing SoC in Battery Cells using Safe Action Perturbations
Authors: E Harshith Kumar Yadav, Rahul Narava, Anshika, Shashi Shekher Jha
Date: 2025-03-11 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning (RL)

Managing equal charge levels in active cell balancing while charging a Li-ion battery is challenging. An imbalance in charge levels affects the state of health of the battery, along with the concerns of thermal runaway and fire hazards. Traditional methods focus on safety assurance as a trade-off between safety and charging time. Others deal with battery-specific conditions to ensure safety, therefore losing on the generalization of the control strategies over various configurations of batteries. In this work, we propose a method to learn safe battery charging actions by using a safety-layer as an add-on over a Deep Reinforcement Learning (RL) agent. The safety layer perturbs the agent's action to prevent the battery from encountering unsafe or dangerous states. Further, our Deep RL framework focuses on learning a generalized policy that can be effectively employed with varying configurations of batteries. Our experimental results demonstrate that the safety-layer based action perturbation incurs fewer safety violations by avoiding unsafe states along with learning a robust policy for several battery configurations.
AI-driven control of bioelectric signalling for real-time topological reorganization of cells
Authors: Gonçalo Hora de Carvalho
Date: 2025-03-10 00:00:00
Tasks: Deep Reinforcement Learning

Understanding and manipulating bioelectric signaling could present a new wave of progress in developmental biology, regenerative medicine, and synthetic biology. Bioelectric signals, defined as voltage gradients across cell membranes caused by ionic movements, play a role in regulating crucial processes including cellular differentiation, proliferation, apoptosis, and tissue morphogenesis. Recent studies demonstrate the ability to modulate these signals to achieve controlled tissue regeneration and morphological outcomes in organisms such as planaria and frogs. However, significant knowledge gaps remain, particularly in predicting and controlling the spatial and temporal dynamics of membrane potentials (V_mem), understanding their regulatory roles in tissue and organ development, and exploring their therapeutic potential in diseases. In this work we propose an experiment using Deep Reinforcement Learning (DRL) framework together with lab automation techniques for real-time manipulation of bioelectric signals to guide tissue regeneration and morphogenesis. The proposed framework should interact continuously with biological systems, adapting strategies based on direct biological feedback. Combining DRL with real-time measurement techniques -- such as optogenetics, voltage-sensitive dyes, fluorescent reporters, and advanced microscopy -- could provide a comprehensive platform for precise bioelectric control, leading to improved understanding of bioelectric mechanisms in morphogenesis, quantitative bioelectric models, identification of minimal experimental setups, and advancements in bioelectric modulation techniques relevant to regenerative medicine and cancer therapy. Ultimately, this research aims to utilize bioelectric signaling to develop new biomedical and bioengineering applications.
Statistical Study of Sensor Data and Investigation of ML-based Calibration Algorithms for Inexpensive Sensor Modules: Experiments from Cape Point
Authors: Travis Barrett, Amit Kumar Mishra
Date: 2025-03-09 00:00:00
Tasks: regression

In this paper we present the statistical analysis of data from inexpensive sensors. We also present the performance of machine learning algorithms when used for automatic calibration such sensors. In this we have used low-cost Non-Dispersive Infrared CO$_2$ sensor placed at a co-located site at Cape Point, South Africa (maintained by Weather South Africa). The collected low-cost sensor data and site truth data are investigated and compared. We compare and investigate the performance of Random Forest Regression, Support Vector Regression, 1D Convolutional Neural Network and 1D-CNN Long Short-Term Memory Network models as a method for automatic calibration and the statistical properties of these model predictions. In addition, we also investigate the drift in performance of these algorithms with time.
ISLS: IoT-Based Smart Lighting System for Improving Energy Conservation in Office Buildings
Authors: Peace Obioma, Obinna Agbodike, Jenhui Chen, Lei Wang
Date: 2025-03-04 00:00:00

With the Internet of Things (IoT) fostering seamless device-to-human and device-to-device interactions, the domain of intelligent lighting systems have evolved beyond simple occupancy and daylight sensing towards autonomous monitoring and control of power consumption and illuminance levels. To this regard, this paper proposes a new do-it-yourself (DIY) IoT-based method of smart lighting system featuring an illuminance control algorithm. The design involves the integration of occupancy and presence sensors alongside a communication module, to enable real-time wireless interaction and remote monitoring of the system parameters from any location through an end-user application. A constrained optimization problem was formulated to determine the optimal dimming vector for achieving target illuminance at minimal power consumption. The simplex algorithm was used to solve this problem, and the system's performance was validated through both MATLAB simulations and real-world prototype testing in an indoor office environment. The obtained experimental results demonstrate substantial power savings across multiple user occupancy scenarios, achieving reductions of approx. 80%, 48%, and 26% for one, two, and four user settings, respectively, in comparison to traditional basic lighting installation systems.
How many simulations do we need for simulation-based inference in cosmology?
Authors: Anirban Bairagi, Benjamin Wandelt, Francisco Villaescusa-Navarro
Date: 2025-03-17 00:00:00

How many simulations do we need to train machine learning methods to extract information available from summary statistics of the cosmological density field? Neural methods have shown the potential to extract non-linear information available from cosmological data. Success depends critically on having sufficient simulations for training the networks and appropriate network architectures. In the first detailed convergence study of neural network training for cosmological inference, we show that currently available simulation suites, such as the Quijote Latin Hypercube(LH) with 2000 simulations, do not provide sufficient training data for a generic neural network to reach the optimal regime, even for the dark matter power spectrum, and in an idealized case. We discover an empirical neural scaling law that predicts how much information a neural network can extract from a highly informative summary statistic, the dark matter power spectrum, as a function of the number of simulations used to train the network, for a wide range of architectures and hyperparameters. We combine this result with the Cramer-Rao information bound to forecast the number of training simulations needed for near-optimal information extraction. To verify our method we created the largest publicly released simulation data set in cosmology, the Big Sobol Sequence(BSQ), consisting of 32,768 $\Lambda$CDM n-body simulations uniformly covering the $\Lambda$CDM parameter space. Our method enables efficient planning of simulation campaigns for machine learning applications in cosmology, while the BSQ dataset provides an unprecedented resource for studying the convergence behavior of neural networks in cosmological parameter inference. Our results suggest that new large simulation suites or new training approaches will be necessary to achieve information-optimal parameter inference from non-linear simulations.
Strategic White Paper on AI Infrastructure for Particle, Nuclear, and Astroparticle Physics: Insights from JENA and EuCAIF
Authors: Sascha Caron, Andreas Ipp, Gert Aarts, Gábor Bíró, Daniele Bonacorsi, Elena Cuoco, Caterina Doglioni, Tommaso Dorigo, Julián García Pardiñas, Stefano Giagu, Tobias Golling, Lukas Heinrich, Ik Siong Heng, Paula Gina Isar, Karolos Potamianos, Liliana Teodorescu, John Veitch, Pietro Vischia, Christoph Weniger
Date: 2025-03-18 00:00:00

Artificial intelligence (AI) is transforming scientific research, with deep learning methods playing a central role in data analysis, simulations, and signal detection across particle, nuclear, and astroparticle physics. Within the JENA communities-ECFA, NuPECC, and APPEC-and as part of the EuCAIF initiative, AI integration is advancing steadily. However, broader adoption remains constrained by challenges such as limited computational resources, a lack of expertise, and difficulties in transitioning from research and development (R&D) to production. This white paper provides a strategic roadmap, informed by a community survey, to address these barriers. It outlines critical infrastructure requirements, prioritizes training initiatives, and proposes funding strategies to scale AI capabilities across fundamental physics over the next five years.
Beyond holography: the entropic quantum gravity foundations of image processing
Authors: Ginestra Bianconi
Date: 2025-03-18 00:00:00

Recently, thanks to the development of artificial intelligence (AI) there is increasing scientific attention to establishing the connections between theoretical physics and AI. Traditionally, these connections have been focusing mostly on the relation between string theory and image processing and involve important theoretical paradigms such as holography. Recently G. Bianconi has proposed the entropic quantum gravity approach that proposes an action for gravity given by the quantum relative entropy between the metrics associated to a manifold. Here it is demonstrated that the famous Perona-Malik algorithm for image processing is the gradient flow of the entropic quantum gravity action. These results provide the geometrical and information theory foundations for the Perona-Malik algorithm and open new avenues for establishing fundamental relations between brain research, machine learning and entropic quantum gravity.
Carbonic anhydrase II simulated with a universal neural network potential
Authors: Timothy T. Duignan
Date: 2025-03-18 00:00:00
Tasks: Drug Design

The carbonic anhydrase II enzyme (CA II) is one of the most significant enzymes in nature, reversibly converting CO$_2$ to bicarbonate at a remarkable rate. The precise mechanism it uses to achieve this rapid turnover remains unclear due to our inability to directly observe or simulate the full process dynamically. Here, we use a recently developed universal neural network potential (Orb) to simulate the active site of CA II. We reproduce several known features of the reaction mechanism, including the proton relay that conducts protons out of the active site to the His64 residue. Additionally, we observe a new reaction pathway where CO$_2$ reacts with a water molecule in the active site, which donates a proton to the zinc-bound hydroxide. This differs from the established mechanism where CO$_2$ directly reacts with hydroxide. Existing experimental data and independent quantum chemistry calculations are used to support the plausibility of this new mechanism. This demonstrates the potential of Orb to efficiently generate novel insights into important molecular scale processes that can potentially be harnessed to improve CO$_2$ capture technologies and drug design.