pwc-archive/papers-with-abstracts huggingface dataset:
Multi-stage model predictive control for slug flow crystallizers using uncertainty-aware surrogate models
Authors: Collin R. Johnson, Stijn de Vries, Kerstin Wohlgemuth, Sergio Lucia
Date: 2025-03-28 00:00:00
Tasks: Model Predictive Control, quantile regression, Uncertainty Quantification

This paper presents a novel dynamic model for slug flow crystallizers that addresses the challenges of spatial distribution without backmixing or diffusion, potentially enabling advanced model-based control. The developed model can accurately describe the main characteristics of slug flow crystallizers, including slug-to-slug variability but leads to a high computational complexity due to the consideration of partial differential equations and population balance equations. For that reason, the model cannot be directly used for process optimization and control. To solve this challenge, we propose two different approaches, conformalized quantile regression and Bayesian last layer neural networks, to develop surrogate models with uncertainty quantification capabilities. These surrogates output a prediction of the system states together with an uncertainty of these predictions to account for process variability and model uncertainty. We use the uncertainty of the predictions to formulate a robust model predictive control approach, enabling robust real-time advanced control of a slug flow crystallizer.
Design and Analysis of a Robust Control System for Triple Inverted Pendulum Stabilization
Authors: Tohid Kargar Tasooji, Sakineh Khodadadi
Date: 2025-03-28 00:00:00

The design of robust controllers for triple inverted pendulum systems presents significant challenges due to their inherent instability and nonlinear dynamics. Furthermore, uncertainties in system parameters further complicate the control design. This paper investigates a robust control strategy for triple inverted pendulums under parameter uncertainty. Two control approaches, namely the $H_\infty$ controller and the $\mu$-synthesis controller, are compared in terms of their ability to achieve reference tracking and disturbance rejection. Simulation results demonstrate that the $H_\infty$ controller provides superior transient performance, making it a promising solution for the robust stabilization of such complex systems.
A Multi-Objective Simultaneous Routing, Facility Location and Allocation Model for Earthquake Emergency Logistics
Authors: Sakineh Khodadadi, Tohid Kargar Tasooji, Afshin Shariat-Mohayman, Navid Kalantari
Date: 2025-03-28 00:00:00

Emergency preparedness reduces the severity and impact of major disasters. In the case of earthquakes, a rapid and efficient emergency response is essential to reduce the number of fatalities. Therefore, the design and planning of an adequate emergency transportation network are crucial in earthquake-prone locations. In the context of emergency transportation modeling, the aim of emergency routing is to find the network with the minimum length that can provide access between the maximum number of Emergency Response Centers (ERCs) and damaged areas. Meanwhile, the goal of the facility location and allocation problem is to optimize the placement of temporary hospitals to increase coverage and accessibility, particularly in remote or severely impacted areas. This paper proposes a multi-objective, robust, multi-modal, and multi-time-period optimization problem that simultaneously optimizes routing, facility location, and hospital allocation. The objective function is to minimize unmet commodity demand, unserved injuries, and economic costs. We adopt a fuzzy goal programming approach to solve the multi-objective simultaneous routing, facility location, and allocation model.
Multi-objective robust controller synthesis with integral quadratic constraints in discrete-time
Authors: Lukas Schwenkel, Johannes Köhler, Matthias A. Müller, Carsten W. Scherer, Frank Allgöwer
Date: 2025-03-28 00:00:00

This article presents a novel framework for the robust controller synthesis problem in discrete-time systems using dynamic Integral Quadratic Constraints (IQCs). We present an algorithm to minimize closed-loop performance measures such as the $\mathcal H_\infty$-norm, the energy-to-peak gain, the peak-to-peak gain, or a multi-objective mix thereof. While IQCs provide a powerful tool for modeling structured uncertainties and nonlinearities, existing synthesis methods are limited to the $\mathcal H_\infty$-norm, continuous-time systems, or special system structures. By minimizing the energy-to-peak and peak-to-peak gain, the proposed synthesis can be utilized to bound the peak of the output, which is crucial in many applications requiring robust constraint satisfaction, input-to-state stability, reachability analysis, or other pointwise-in-time bounds. Numerical examples demonstrate the robustness and performance of the controllers synthesized with the proposed algorithm.
Reinforcement learning for efficient and robust multi-setpoint and multi-trajectory tracking in bioprocesses
Authors: Sebastián Espinel-Ríos, José L. Avalos, Ehecatl Antonio del Rio Chanona, Dongda Zhang
Date: 2025-03-28 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

Efficient and robust bioprocess control is essential for maximizing performance and adaptability in advanced biotechnological systems. In this work, we present a reinforcement-learning framework for multi-setpoint and multi-trajectory tracking. Tracking multiple setpoints and time-varying trajectories in reinforcement learning is challenging due to the complexity of balancing multiple objectives, a difficulty further exacerbated by system uncertainties such as uncertain initial conditions and stochastic dynamics. This challenge is relevant, e.g., in bioprocesses involving microbial consortia, where precise control over population compositions is required. We introduce a novel return function based on multiplicative reciprocal saturation functions, which explicitly couples reward gains to the simultaneous satisfaction of multiple references. Through a case study involving light-mediated cybergenetic growth control in microbial consortia, we demonstrate via computational experiments that our approach achieves faster convergence, improved stability, and superior control compliance compared to conventional quadratic-cost-based return functions. Moreover, our method enables tuning of the saturation function's parameters, shaping the learning process and policy updates. By incorporating system uncertainties, our framework also demonstrates robustness, a key requirement in industrial bioprocessing. Overall, this work advances reinforcement-learning-based control strategies in bioprocess engineering, with implications in the broader field of process and systems engineering.
Smart Sensing Breaks the Accuracy Barrier in Battery State Monitoring
Authors: Xiaolei Bian, Changfu Zou, Björn Fridholm, Christian Sundvall, Torsten Wik
Date: 2025-03-28 00:00:00

Accurate state-of-charge (SOC) estimation is essential for optimizing battery performance, ensuring safety, and maximizing economic value. Conventional current and voltage measurements, however, have inherent limitations in fully inferring the multiphysics-resolved dynamics inside battery cells. This creates an accuracy barrier that constrains battery usage and reduces cost-competitiveness and sustainability across industries dependent on battery technology. In this work, we introduce an integrated sensor framework that combines novel mechanical, thermal, gas, optical, and electrical sensors with traditional measurements to break through this barrier. We generate three unique datasets with eleven measurement types and propose an explainable machine-learning approach for SOC estimation. This approach renders the measured signals and the predictive result of machine learning physically interpretable with respect to battery SOC, offering fundamental insights into the time-varying importance of different signals. Our experimental results reveal a marked increase in SOC estimation accuracy--enhanced from 46.1% to 74.5%--compared to conventional methods. This approach not only advances SOC monitoring precision but also establishes a foundation for monitoring additional battery states to further improve safety, extend lifespan, and facilitate fast charging.
Small-gain conditions for exponential incremental stability in feedback interconnections
Authors: Mohamed Yassine Arkhis, Denis Efimov
Date: 2025-03-28 00:00:00

We prove that under a small-gain condition, an interconnection of two globally incrementally exponentially stable systems inherits this property on any compact connected forward invariant set. It is also demonstrated that the interconnection inherits a weaker version of incremental exponential stability globally. An example illustrating the theoretical findings is given. The example also shows that the uniform negativity of the Jacobian is not necessary for incremental exponential stability.
Open-loop control design for contraction in affine nonlinear systems
Authors: Mohamed Yassine Arkhis, Denis Efimov
Date: 2025-03-28 00:00:00

In this paper, first, it is shown that if a nonlinear time-varying system is contractive, then it is incrementally exponentially stable. Second, leveraging this result, under mild restrictions, an approach is proposed to design feedforward inputs for affine in control systems providing contraction/incremental exponential stability. Unlike standard stability notions, which have well-established control design techniques, this note can be considered among the first ones to provide such a tool for a kind of incremental stability. The theoretical findings are illustrated by examples.
Generalised Harmonic Domain Analysis for Transformer Core Hysteresis Modelling
Authors: Josh Schipper, Radnya Mukhedkar, Neville Watson, Veerabrahmam Bathini, Jan Meyer
Date: 2025-03-28 00:00:00

This work identifies the general approach for linearising any power system component in the harmonic domain, that is with respect to its Fourier series coefficients. This ability enables detailed harmonic analysis, and is key as more power electronic devices inject harmonic currents into the power system to its shared detriment. The general approach requires a time domain model of the component, and is most applicable where a conversion to the frequency domain is impractical prior to linearisation. The outcome is a Norton equivalent current source, which expresses linear coupling between harmonic frequencies with admittance matrices. These are the so-called frequency coupling matrices. The general approach is demonstrated for magnetic hysteresis, where a Preisach model has been developed for this purpose. A new data driven approach is used to fit the test results of a small physical transformer to the Preisach model. Results show an improved accuracy in the frequency coupling matrices over models that only considered magnetic saturation. Maximum improvement is observed in the odd harmonic current to odd harmonic voltage couplings.
Towards explainable data-driven predictive control with regularizations
Authors: Manuel Klädtke, Moritz Schulze Darup
Date: 2025-03-27 00:00:00
Tasks: Model Predictive Control

Data-driven predictive control (DPC), using linear combinations of recorded trajectory data, has recently emerged as a popular alternative to traditional model predictive control (MPC). Without an explicitly enforced prediction model, the effects of commonly used regularization terms (and the resulting predictions) can be opaque. This opacity may lead to practical challenges, such as reliance on empirical tuning of regularization parameters based on closed-loop performance, and potentially misleading heuristic interpretations of norm-based regularizations. However, by examining the structure of the underlying optimal control problem (OCP), more precise and insightful interpretations of regularization effects can be derived. In this paper, we demonstrate how to analyze the predictive behavior of DPC through implicit predictors and the trajectory-specific effects of quadratic regularization. We further extend these results to cover typical DPC modifications, including DPC for affine systems, offset regularizations, slack variables, and terminal constraints. Additionally, we provide a simple but general result on (recursive) feasibility in DPC. This work aims to enhance the explainability and reliability of DPC by providing a deeper understanding of these regularization mechanisms.
PowerGNN: A Topology-Aware Graph Neural Network for Electricity Grids
Authors: Dhruv Suri, Mohak Mangal
Date: 2025-03-26 00:00:00
Tasks: Graph Neural Network

The increasing penetration of renewable energy sources introduces significant variability and uncertainty in modern power systems, making accurate state prediction critical for reliable grid operation. Conventional forecasting methods often neglect the power grid's inherent topology, limiting their ability to capture complex spatio temporal dependencies. This paper proposes a topology aware Graph Neural Network (GNN) framework for predicting power system states under high renewable integration. We construct a graph based representation of the power network, modeling buses and transmission lines as nodes and edges, and introduce a specialized GNN architecture that integrates GraphSAGE convolutions with Gated Recurrent Units (GRUs) to model both spatial and temporal correlations in system dynamics. The model is trained and evaluated on the NREL 118 test system using realistic, time synchronous renewable generation profiles. Our results show that the proposed GNN outperforms baseline approaches including fully connected neural networks, linear regression, and rolling mean models, achieving substantial improvements in predictive accuracy. The GNN achieves average RMSEs of 0.13 to 0.17 across all predicted variables and demonstrates consistent performance across spatial locations and operational conditions. These results highlight the potential of topology aware learning for scalable and robust power system forecasting in future grids with high renewable penetration.
Symmetry-Informed Graph Neural Networks for Carbon Dioxide Isotherm and Adsorption Prediction in Aluminum-Substituted Zeolites
Authors: Marko Petković, José-Manuel Vicent Luna, Elīza Beate Dinne, Vlado Menkovski, Sofía Calero
Date: 2025-03-26 00:00:00
Tasks: Graph Neural Network, Property Prediction

Accurately predicting adsorption properties in nanoporous materials using Deep Learning models remains a challenging task. This challenge becomes even more pronounced when attempting to generalize to structures that were not part of the training data.. In this work, we introduce SymGNN, a graph neural network architecture that leverages material symmetries to improve adsorption property prediction. By incorporating symmetry operations into the message-passing mechanism, our model enhances parameter sharing across different zeolite topologies, leading to improved generalization. We evaluate SymGNN on both interpolation and generalization tasks, demonstrating that it successfully captures key adsorption trends, including the influence of both the framework and aluminium distribution on CO$_2$ adsorption. Furthermore, we apply our model to the characterization of experimental adsorption isotherms, using a genetic algorithm to infer likely aluminium distributions. Our results highlight the effectiveness of machine learning models trained on simulations for studying real materials and suggest promising directions for fine-tuning with experimental data and generative approaches for the inverse design of multifunctional nanomaterials.
Simulating cell populations with explicit cell cycle length -- implications to cell cycle dependent tumour therapy
Authors: Peter Boldog, Gergely Röst
Date: 2025-03-29 00:00:00
Tasks: parameter estimation

In this study, we present a stochastic simulation model designed to explicitly incorporate cell cycle length, overcoming limitations associated with classical compartmental models. Our approach employs a delay mechanism to represent the cell cycle, allowing the use of arbitrary distributions for cell cycle lengths. We demonstrate the feasibility of our model by fitting it to experimental data from melanoma cell lines previously studied by Vittadello et al. Notably, our model successfully replicates experimentally observed synchronization phenomena that multi-stage models could not adequately explain. By using a gamma distribution to model cell cycle lengths, we achieved excellent agreement between our simulations and empirical data, while significantly reducing computational complexity and parameter estimation challenges inherent in multi-stage approaches. Our results highlight the importance of explicitly incorporating cell cycle lengths in modeling cell populations, with potential implications for optimizing cell cycle-dependent tumor therapies.
Forecasting Volcanic Radiative Power (VPR) at Fuego Volcano Using Bayesian Regularized Neural Network
Authors: Snehamoy Chatterjee, Greg Waite, Sidike Paheding, Luke Bowman
Date: 2025-03-25 00:00:00

Forecasting volcanic activity is critical for hazard assessment and risk mitigation. Volcanic Radiative Power (VPR), derived from thermal remote sensing data, serves as an essential indicator of volcanic activity. In this study, we employ Bayesian Regularized Neural Networks (BRNN) to predict future VPR values based on historical data from Fuego Volcano, comparing its performance against Scaled Conjugate Gradient (SCG) and Levenberg-Marquardt (LM) models. The results indicate that BRNN outperforms SCG and LM, achieving the lowest mean squared error (1.77E+16) and the highest R-squared value (0.50), demonstrating its superior ability to capture VPR variability while minimizing overfitting. Despite these promising results, challenges remain in improving the model's predictive accuracy. Future research should focus on integrating additional geophysical parameters, such as seismic and gas emission data, to enhance forecasting precision. The findings highlight the potential of machine learning models, particularly BRNN, in advancing volcanic activity forecasting, contributing to more effective early warning systems for volcanic hazards.
Asymmetry in Distributions of Accumulated Gains and Losses in Stock Returns
Authors: Hamed Farahani, R. A. Serota
Date: 2025-03-31 00:00:00

We study decades-long historic distributions of accumulated S\&P500 returns, from daily returns to those over several weeks. The time series of the returns emphasize major upheavals in the markets -- Black Monday, Tech Bubble, Financial Crisis and Covid Pandemic -- which are reflected in the tail ends of the distributions. De-trending the overall gain, we concentrate on comparing distributions of gains and losses. Specifically, we compare the tails of the distributions, which are believed to exhibit power-law behavior and possibly contain outliers. Towards this end we find confidence intervals of the linear fits of the tails of the complementary cumulative distribution functions on a log-log scale, as well as conduct a statistical U-test in order to detect outliers. We also study probability density functions of the full distributions of the returns with the emphasis on their asymmetry. The key empirical observations are that the mean of de-trended distributions increases near-linearly with the number of days of accumulation while the overall skew is negative -- consistent with the heavier tails of losses -- and depends little on the number of days of accumulation. At the same time the variance of the distributions exhibits near-perfect linear dependence on the number of days of accumulation, that is it remains constant if scaled to the latter. Finally, we discuss the theoretical framework for understanding accumulated returns. Our main conclusion is that the current state of theory, which predicts symmetric or near-symmetric distributions of returns cannot explain the aggregate of empirical results.
From Occurrence to Consequence: A Comprehensive Data-driven Analysis of Building Fire Risk
Authors: Chenzhi Ma, Hongru Du, Shengzhi Luan, Ensheng Dong, Lauren M. Gardner, Thomas Gernay
Date: 2025-03-11 00:00:00

Building fires pose a persistent threat to life, property, and infrastructure, emphasizing the need for advanced risk mitigation strategies. This study presents a data-driven framework analyzing U.S. fire risks by integrating over one million fire incident reports with diverse fire-relevant datasets, including social determinants, building inventories, weather conditions, and incident-specific factors. By adapting machine learning models, we identify key risk factors influencing fire occurrence and consequences. Our findings show that vulnerable communities, characterized by socioeconomic disparities or the prevalence of outdated or vacant buildings, face higher fire risks. Incident-specific factors, such as fire origins and safety features, strongly influence fire consequences. Buildings equipped with fire detectors and automatic extinguishing systems experience significantly lower fire spread and injury risks. By pinpointing high-risk areas and populations, this research supports targeted interventions, including mandating fire safety systems and providing subsidies for disadvantaged communities. These measures can enhance fire prevention, protect vulnerable groups, and promote safer, more equitable communities.
SeisRDT: Latent Diffusion Model Based On Representation Learning For Seismic Data Interpolation And Reconstruction
Authors: Shuang Wang, Fei Deng, Peifan Jiang, Zezheng Ni, Bin Wang
Date: 2025-03-17 00:00:00
Tasks: Data Compression, Representation Learning

Due to limitations such as geographic, physical, or economic factors, collected seismic data often have missing traces. Traditional seismic data reconstruction methods face the challenge of selecting numerous empirical parameters and struggle to handle large-scale continuous missing traces. With the advancement of deep learning, various diffusion models have demonstrated strong reconstruction capabilities. However, these UNet-based diffusion models require significant computational resources and struggle to learn the correlation between different traces in seismic data. To address the complex and irregular missing situations in seismic data, we propose a latent diffusion transformer utilizing representation learning for seismic data reconstruction. By employing a mask modeling scheme based on representation learning, the representation module uses the token sequence of known data to infer the token sequence of unknown data, enabling the reconstructed data from the diffusion model to have a more consistent data distribution and better correlation and accuracy with the known data. We propose the Representation Diffusion Transformer architecture, and a relative positional bias is added when calculating attention, enabling the diffusion model to achieve global modeling capability for seismic data. Using a pre-trained data compression model compresses the training and inference processes of the diffusion model into a latent space, which, compared to other diffusion model-based reconstruction methods, reduces computational and inference costs. Reconstruction experiments on field and synthetic datasets indicate that our method achieves higher reconstruction accuracy than existing methods and can handle various complex missing scenarios.
Multiple Embeddings for Quantum Machine Learning
Authors: Siyu Han, Lihan Jia, LanZhe Guo
Date: 2025-03-27 00:00:00
Tasks: Diversity, Quantum Machine Learning

This work focuses on the limitations about the insufficient fitting capability of current quantum machine learning methods, which results from the over-reliance on a single data embedding strategy. We propose a novel quantum machine learning framework that integrates multiple quantum data embedding strategies, allowing the model to fully exploit the diversity of quantum computing when processing various datasets. Experimental results validate the effectiveness of the proposed framework, demonstrating significant improvements over existing state-of-the-art methods and achieving superior performance in practical applications.