pwc-archive/papers-with-abstracts huggingface dataset:
A Supervised Machine-Learning Approach For Turboshaft Engine Dynamic Modeling Under Real Flight Conditions
Authors: Damiano Paniccia, Francesco Aldo Tucci, Joel Guerrero, Luigi Capone, Nicoletta Sanguini, Tommaso Benacchio, Luigi Bottasso
Date: 2025-02-19 00:00:00

Rotorcraft engines are highly complex, nonlinear thermodynamic systems that operate under varying environmental and flight conditions. Simulating their dynamics is crucial for design, fault diagnostics, and deterioration control phases, and requires robust and reliable control systems to estimate engine performance throughout flight envelope. However, the development of detailed physical models of the engine based on numerical simulations is a very challenging task due to the complex and entangled physics driving the engine. In this scenario, data-driven machine-learning techniques are of great interest to the aircraft engine community, due to their ability to describe nonlinear systems' dynamic behavior and enable online performance estimation, achieving excellent results with accuracy competitive with the state of the art. In this work, we explore different Neural Network architectures to model the turboshaft engine of Leonardo's AW189P4 prototype, aiming to predict the engine torque. The models are trained on an extensive database of real flight tests featuring a variety of operational maneuvers performed under different flight conditions, providing a comprehensive representation of the engine's performance. To complement the neural network approach, we apply Sparse Identification of Nonlinear Dynamics (SINDy) to derive a low-dimensional dynamical model from the available data, describing the relationship between fuel flow and engine torque. The resulting model showcases SINDy's capability to recover the actual physics underlying the engine dynamics and demonstrates its potential for investigating more complex aspects of the engine. The results prove that data-driven engine models can exploit a wider range of parameters than standard transfer function-based approaches, enabling the use of trained schemes to simulate nonlinear effects in different engines and helicopters.
Comprehensive Review on the Control of Heat Pumps for Energy Flexibility in Distribution Networks
Authors: Gustavo L. Aschidamini, Mina Pavlovic, Bradley A. Reinholz, Malcolm S. Metcalfe, Taco Niet, Mariana Resener
Date: 2025-02-19 00:00:00
Tasks: Model Predictive Control, Reinforcement Learning (RL), Scheduling

Decarbonization plans promote the transition to heat pumps (HPs), creating new opportunities for their energy flexibility in demand response programs, solar photovoltaic integration and optimization of distribution networks. This paper reviews scheduling-based and real-time optimization methods for controlling HPs with a focus on energy flexibility in distribution networks. Scheduling-based methods fall into two categories: rule-based controllers (RBCs), which rely on predefined control rules without explicitly seeking optimal solutions, and optimization models, which are designed to determine the optimal scheduling of operations. Real-time optimization is achieved through model predictive control (MPC), which relies on a predictive model to optimize decisions over a time horizon, and reinforcement learning (RL), which takes a model-free approach by learning optimal strategies through direct interaction with the environment. The paper also examines studies on the impact of HPs on distribution networks, particularly those leveraging energy flexibility strategies. Key takeaways suggest the need to validate control strategies for extreme cold-weather regions that require backup heaters, as well as develop approaches designed for demand charge schemes that integrate HPs with other controllable loads. From a grid impact assessment perspective, studies have focused primarily on RBCs for providing energy flexibility through HP operation, without addressing more advanced methods such as real-time optimization using MPC or RL-based algorithms. Incorporating these advanced control strategies could help identify key limitations, including the impact of varying user participation levels and the cost-benefit trade-offs associated with their implementation.
Experiment Design with Gaussian Process Regression with Applications to Chance-Constrained Control
Authors: Sean Anderson, Katie Byl, João P. Hespanha
Date: 2025-02-19 00:00:00
Tasks: Experimental Design, Gaussian Processes, Uncertainty Quantification

Learning for control in repeated tasks allows for well-designed experiments to gather the most useful data. We consider the setting in which we use a data-driven controller that does not have access to the true system dynamics. Rather, the controller uses inferred dynamics based on the available information. In order to acquire data that is beneficial for this controller, we present an experimental design approach that leverages the current data to improve expected control performance. We focus on the setting in which inference on the unknown dynamics is performed using Gaussian processes. Gaussian processes not only provide uncertainty quantification but also allow us to leverage structures inherent to Gaussian random variables. Through this structure, we design experiments via gradient descent on the expected control performance with respect to the experiment input. In particular, we focus on a chance-constrained minimum expected time control problem. Numerical demonstrations of our approach indicate our experimental design outperforms relevant benchmarks.
OBELiX: A Curated Dataset of Crystal Structures and Experimentally Measured Ionic Conductivities for Lithium Solid-State Electrolytes
Authors: Félix Therrien, Jamal Abou Haibeh, Divya Sharma, Rhiannon Hendley, Alex Hernández-García, Sun Sun, Alain Tchagang, Jiang Su, Samuel Huberman, Yoshua Bengio, Hongyu Guo, Homin Shin
Date: 2025-02-20 00:00:00

Solid-state electrolyte batteries are expected to replace liquid electrolyte lithium-ion batteries in the near future thanks to their higher theoretical energy density and improved safety. However, their adoption is currently hindered by their lower effective ionic conductivity, a quantity that governs charge and discharge rates. Identifying highly ion-conductive materials using conventional theoretical calculations and experimental validation is both time-consuming and resource-intensive. While machine learning holds the promise to expedite this process, relevant ionic conductivity and structural data is scarce. Here, we present OBELiX, a domain-expert-curated database of $\sim$600 synthesized solid electrolyte materials and their experimentally measured room temperature ionic conductivities gathered from literature. Each material is described by their measured composition, space group and lattice parameters. A full-crystal description in the form of a crystallographic information file (CIF) is provided for ~320 structures for which atomic positions were available. We discuss various statistics and features of the dataset and provide training and testing splits that avoid data leakage. Finally, we benchmark seven existing ML models on the task of predicting ionic conductivity and discuss their performance. The goal of this work is to facilitate the use of machine learning for solid-state electrolyte materials discovery.
A functional exchange shunt in the umbilical cord: the role of coiling in solute and heat transfer
Authors: Tianran Wan, Edward D. Johnstone, Shier Nee Saw, Oliver E. Jensen, Igor L. Chernyavsky
Date: 2025-02-20 00:00:00

The umbilical cord plays a critical role in delivering nutrients and oxygen from the placenta to the fetus through the umbilical vein, while the two umbilical arteries carry deoxygenated blood with waste products back to the placenta. Although solute exchange in the placenta has been extensively studied, exchange within the cord tissue has not been investigated. Here, we explore the hypothesis that the coiled structure of the umbilical cord could strengthen diffusive coupling between the arteries and the vein, resulting in a functional shunt. We calculate the diffusion of solutes, such as oxygen, and heat in the umbilical cord to quantify how this shunt is affected by vascular configuration within the cord. We demonstrate that the shunt is enhanced by coiling and vessel proximity. Furthermore, our model predicts that typical vascular configurations of the human cord tend to minimise shunting, which could otherwise disrupt thermal regulation of the fetus. We also show that the exchange, amplified by coiling, can provide additional oxygen supply to the cord tissue surrounding the umbilical vessels.
Learning the P2D Model for Lithium-Ion Batteries with SOH Detection
Authors: Maricela Best McKay, Bhushan Gopaluni, Brian Wetton
Date: 2025-02-19 00:00:00
Tasks: Management

Lithium ion batteries are widely used in many applications. Battery management systems control their optimal use and charging and predict when the battery will cease to deliver the required output on a planned duty or driving cycle. Such systems use a simulation of a mathematical model of battery performance. These models can be electrochemical or data-driven. Electrochemical models for batteries running at high currents are mathematically and computationally complex. In this work, we show that a well-regarded electrochemical model, the Pseudo Two Dimensional (P2D) model, can be replaced by a computationally efficient Convolutional Neural Network (CNN) surrogate model fit to accurately simulated data from a class of random driving cycles. We demonstrate that a CNN is an ideal choice for accurately capturing Lithium ion concentration profiles. Additionally, we show how the neural network model can be adjusted to correspond to battery changes in State of Health (SOH).
Causality Analysis of COVID-19 Induced Crashes in Stock and Commodity Markets: A Topological Perspective
Authors: Buddha Nath Sharma, Anish rai, SR Luwang, Md. Nurujjaman, Sushovan Majhi
Date: 2025-02-20 00:00:00
Tasks: Topological Data Analysis

The paper presents a comprehensive causality analysis of the US stock and commodity markets during the COVID-19 crash. The dynamics of different sectors are also compared. We use Topological Data Analysis (TDA) on multidimensional time-series to identify crashes in stock and commodity markets. The Wasserstein Distance WD shows distinct spikes signaling the crash for both stock and commodity markets. We then compare the persistence diagrams of stock and commodity markets using the WD metric. A significant spike in the $WD$ between stock and commodity markets is observed during the crisis, suggesting significant topological differences between the markets. Similar spikes are observed between the sectors of the US market as well. Spikes obtained may be due to either a difference in the magnitude of crashes in the two markets (or sectors), or from the temporal lag between the two markets suggesting information flow. We study the Granger-causality between stock and commodity markets and also between different sectors. The results show a bidirectional Granger-causality between commodity and stock during the crash period, demonstrating the greater interdependence of financial markets during the crash. However, the overall analysis shows that the causal direction is from stock to commodity. A pairwise Granger-causal analysis between US sectors is also conducted. There is a significant increase in the interdependence between the sectors during the crash period. TDA combined with Granger-causality effectively analyzes the interdependence and sensitivity of different markets and sectors.
Synthetic Porous Microstructures: Automatic Design, Simulation, and Permeability Analysis
Authors: Thomas Lavigne, Camilo Andrés Suarez Afanador, Anas Obeidat, Stéphane Urcun
Date: 2025-02-20 00:00:00
Tasks: Benchmarking

This study introduces an open-source computational framework for the generation and permeability evaluation of synthetic porous media. The proposed methodology integrates crystallographic and meshing tools to construct controlled microstructures with tunable porosity, facilitating seamless transitions from geometric modelling to computational domains for numerical simulations. The generated structures are analysed through fluid-structure interaction (FSI) simulations, leveraging the Entropically Damped Artificial Compressibility (EDAC) formulation in conjunction with the Discretization-Corrected Particle Strength Exchange (DC-PSE) method. A novel approach for the numerical estimation of the macroscopic permeability tensor is presented, employing a stochastic upscaling technique inspired by the volume averaging method. To validate the framework, we investigate the transition between creeping and non-Darcy flow regimes through parametric permeability studies, utilizing a one-dimensional approach for practical benchmarking. The results establish a foundation for experimental validation and provide insights into the customised design of porous structures for engineering and biomedical applications, offering a versatile tool for research in fluid transport and porous media mechanics.
Risk-mediated dynamic regulation of effective contacts de-synchronizes outbreaks in metapopulation epidemic models
Authors: Henrik Zunker, Philipp Dönges, Patrick Lenz, Seba Contreras, Martin J. Kühn
Date: 2025-02-20 00:00:00

Metapopulation epidemic models help capture the spatial dimension of infectious disease spread by dividing heterogeneous populations into separate but interconnected communities, represented by nodes in a network. In the event of an epidemic, an important research question is, to what degree spatial information (i.e., regional or national) is relevant for mitigation and (local) policymakers. This study investigates the impact of different levels of information on nationwide epidemic outcomes, modeling the reaction to the measured hazard as a feedback loop reducing contact rates in a metapopulation model based on ordinary differential equations (ODEs). Using COVID-19 and high-resolution mobility data for Germany of 2020 as a case study, we found two markedly different regimes depending on the maximum contact reduction $\psi_{\rm max}$: mitigation and suppression. In the regime of (modest) mitigation, gradually increasing $\psi_{\rm max}$ from zero to moderate levels delayed and spread out the onset of infection waves while gradually reducing the peak values. This effect was more pronounced when the contribution of regional information was low relative to national data. In the suppression regime, the feedback-induced contact reduction is strong enough to extinguish local outbreaks and decrease the mean and variance of the peak day distribution, thus regional information was more important. When suppression or elimination is impossible, ensuring local epidemics are desynchronized helps to avoid hospitalization or intensive care bottlenecks by reallocating resources from less-affected areas.
Provable Quantum Algorithm Advantage for Gaussian Process Quadrature
Authors: Cristian A. Galvis-Florez, Ahmad Farooq, Simo Särkkä
Date: 2025-02-20 00:00:00
Tasks: Gaussian Processes, Numerical Integration

The aim of this paper is to develop novel quantum algorithms for Gaussian process quadrature methods. Gaussian process quadratures are numerical integration methods where Gaussian processes are used as functional priors for the integrands to capture the uncertainty arising from the sparse function evaluations. Quantum computers have emerged as potential replacements for classical computers, offering exponential reductions in the computational complexity of machine learning tasks. In this paper, we combine Gaussian process quadratures and quantum computing by proposing a quantum low-rank Gaussian process quadrature method based on a Hilbert space approximation of the Gaussian process kernel and enhancing the quadrature using a quantum circuit. The method combines the quantum phase estimation algorithm with the quantum principal component analysis technique to extract information up to a desired rank. Then, Hadamard and SWAP tests are implemented to find the expected value and variance that determines the quadrature. We use numerical simulations of a quantum computer to demonstrate the effectiveness of the method. Furthermore, we provide a theoretical complexity analysis that shows a polynomial advantage over classical Gaussian process quadrature methods. The code is available at https://github.com/cagalvisf/Quantum_HSGPQ.
Purest Quantum State Identification
Authors: Yingqi Yu, Honglin Chen, Jun Wu, Wei Xie, Xiangyang Li
Date: 2025-02-20 00:00:00

Precise identification of quantum states under noise constraints is essential for quantum information processing. In this study, we generalize the classical best arm identification problem to quantum domains, designing methods for identifying the purest one within $K$ unknown $n$-qubit quantum states using $N$ samples. %, with direct applications in quantum computation and quantum communication. We propose two distinct algorithms: (1) an algorithm employing incoherent measurements, achieving error $\exp\left(- \Omega\left(\frac{N H_1}{\log(K) 2^n }\right) \right)$, and (2) an algorithm utilizing coherent measurements, achieving error $\exp\left(- \Omega\left(\frac{N H_2}{\log(K) }\right) \right)$, highlighting the power of quantum memory. Furthermore, we establish a lower bound by proving that all strategies with fixed two-outcome incoherent POVM must suffer error probability exceeding $ \exp\left( - O\left(\frac{NH_1}{2^n}\right)\right)$. This framework provides concrete design principles for overcoming sampling bottlenecks in quantum technologies.
Towards efficient quantum algorithms for diffusion probability models
Authors: Yunfei Wang, Ruoxi Jiang, Yingda Fan, Xiaowei Jia, Jens Eisert, Junyu Liu, Jin-Peng Liu
Date: 2025-02-20 00:00:00
Tasks: Audio Generation

A diffusion probabilistic model (DPM) is a generative model renowned for its ability to produce high-quality outputs in tasks such as image and audio generation. However, training DPMs on large, high-dimensional datasets such as high-resolution images or audio incurs significant computational, energy, and hardware costs. In this work, we introduce efficient quantum algorithms for implementing DPMs through various quantum ODE solvers. These algorithms highlight the potential of quantum Carleman linearization for diverse mathematical structures, leveraging state-of-the-art quantum linear system solvers (QLSS) or linear combination of Hamiltonian simulations (LCHS). Specifically, we focus on two approaches: DPM-solver-$k$ which employs exact $k$-th order derivatives to compute a polynomial approximation of $\epsilon_\theta(x_\lambda,\lambda)$; and UniPC which uses finite difference of $\epsilon_\theta(x_\lambda,\lambda)$ at different points $(x_{s_m}, \lambda_{s_m})$ to approximate higher-order derivatives. As such, this work represents one of the most direct and pragmatic applications of quantum algorithms to large-scale machine learning models, presumably talking substantial steps towards demonstrating the practical utility of quantum computing.
An Empirical Risk Minimization Approach for Offline Inverse RL and Dynamic Discrete Choice Model
Authors: Enoch H. Kang, Hema Yoganarasimhan, Lalit Jain
Date: 2025-02-19 00:00:00

We study the problem of estimating Dynamic Discrete Choice (DDC) models, also known as offline Maximum Entropy-Regularized Inverse Reinforcement Learning (offline MaxEnt-IRL) in machine learning. The objective is to recover reward or $Q^*$ functions that govern agent behavior from offline behavior data. In this paper, we propose a globally convergent gradient-based method for solving these problems without the restrictive assumption of linearly parameterized rewards. The novelty of our approach lies in introducing the Empirical Risk Minimization (ERM) based IRL/DDC framework, which circumvents the need for explicit state transition probability estimation in the Bellman equation. Furthermore, our method is compatible with non-parametric estimation techniques such as neural networks. Therefore, the proposed method has the potential to be scaled to high-dimensional, infinite state spaces. A key theoretical insight underlying our approach is that the Bellman residual satisfies the Polyak-Lojasiewicz (PL) condition -- a property that, while weaker than strong convexity, is sufficient to ensure fast global convergence guarantees. Through a series of synthetic experiments, we demonstrate that our approach consistently outperforms benchmark methods and state-of-the-art alternatives.
Secondary materials, Pigouvian taxes, and a monopsony
Authors: Timo Kuosmanen, Xun Zhou
Date: 2025-02-20 00:00:00

Secondary materials present promising opportunities for firms to repurpose emissions into marketable goods, aligning with circular economy principles. This paper examines conditions under which introducing a market for secondary materials can completely replace Pigouvian emissions taxes. These conditions prove highly restrictive: positive Pigouvian emissions taxes remain necessary unless secondary materials prices immediately reach unrealistically high levels. We propose that the socially optimal budget-neutral policy is to subsidize secondary materials prices while taxing uncontrolled emissions. Further, we extend the analysis to a two-firm framework where a data center supplies residual heat to a district heating firm acting as a monopsony buyer. This extension explicitly models the demand for residual heat and explores how subsidies and emissions taxes align firm incentives with the social optimum in the absence of competitive markets.
SOE's ESG Performance on Financial Flexibility: The Evidence from the Hong Kong Stock Market
Authors: Yan Li
Date: 2025-02-20 00:00:00

As the global economic environment becomes increasingly unstable, enhancing financial flexibility to cope with risks has become the consensus of many companies. At the same time, environmental, social, and governance (ESG) performance may be one of the effective ways. We studied the impact of a firm's ESG performance on its financial flexibility with a sample of companies listed on the Hong Kong stock market from 2018 to 2022. The empirical results show that good environmental, social and governance performance can significantly improve a firm's financial flexibility. In addition, this paper also finds that the influence of ESG performance on financial flexibility is weak for state-owned enterprises due to the influence of governance structure and market characteristics. Finally, the further analysis shows that there is a mediating role played by financing constraints in this process. This study can provide background information for state-owned enterprises' governance, information disclosure, and corporate operations. It also has guiding significance for relevant investors, management and officials.
Community Bank Establishment and Consumption Growth: Evidence from Panel Study of Income Dynamics in USA
Authors: Yan Li
Date: 2025-02-20 00:00:00

Consumption is a primary source of economic growth and key indicator of poverty. The establishment of community banks can provide credit resources, unlocking household consumption potential and playing a crucial role in economic development. This study explores the role of community banks in promoting consumption by using data from the Panel Study of Income Dynamics (PSID) for 11 waves from 1980 to 1990, and constructing a fixed-effects model using the time-varying difference-in-differences (DID) method. The findings indicate that the establishment of community banks effectively stimulates growth in local household consumption, primarily by increasing household income and reducing precautionary savings. Therefore, both government and financial institutions should further promote the development of regional financial institutions and credit tools to promote economic growth.