pwc-archive/papers-with-abstracts huggingface dataset:
Anomalous transport models for fluid classification: insights from an experimentally driven approach
Authors: Sara Bernardi, Paolo Begnamino, Marco Pizzi, Lamberto Rondoni
Date: 2025-04-09 00:00:00

In recent years, research and development in nanoscale science and technology have grown significantly, with electrical transport playing a key role. A natural challenge for its description is to shed light on anomalous behaviours observed in a variety of low-dimensional systems. We use a synergistic combination of experimental and mathematical modelling to explore the transport properties of the electrical discharge observed within a micro-gap based sensor immersed in fluids with different insulating properties. Data from laboratory experiments are collected and used to inform and calibrate four mathematical models that comprise partial differential equations describing different kinds of transport, including anomalous diffusion: the Gaussian Model with Time Dependent Diffusion Coefficient, the Porous Medium Equation, the Kardar-Parisi-Zhang Equation and the Telegrapher Equation. Performance analysis of the models through data fitting reveals that the Gaussian Model with a Time-Dependent Diffusion Coefficient most effectively describes the observed phenomena. This model proves particularly valuable in characterizing the transport properties of electrical discharges when the micro-electrodes are immersed in a wide range of insulating as well as conductive fluids. Indeed, it can suitably reproduce a range of behaviours spanning from clogging to bursts, allowing accurate and quite general fluid classification. Finally, we apply the data-driven mathematical modeling approach to ethanol-water mixtures. The results show the model's potential for accurate prediction, making it a promising method for analyzing and classifying fluids with unknown insulating properties.
Hybrid machine learning models based on physical patterns to accelerate CFD simulations: a short guide on autoregressive models
Authors: Arindam Sengupta, Rodrigo Abadía-Heredia, Ashton Hetherington, José Miguel Pérez, Soledad Le Clainche
Date: 2025-04-09 00:00:00
Tasks: Dimensionality Reduction, Hybrid Machine Learning

Accurate modeling of the complex dynamics of fluid flows is a fundamental challenge in computational physics and engineering. This study presents an innovative integration of High-Order Singular Value Decomposition (HOSVD) with Long Short-Term Memory (LSTM) architectures to address the complexities of reduced-order modeling (ROM) in fluid dynamics. HOSVD improves the dimensionality reduction process by preserving multidimensional structures, surpassing the limitations of Singular Value Decomposition (SVD). The methodology is tested across numerical and experimental data sets, including two- and three-dimensional (2D and 3D) cylinder wake flows, spanning both laminar and turbulent regimes. The emphasis is also on exploring how the depth and complexity of LSTM architectures contribute to improving predictive performance. Simpler architectures with a single dense layer effectively capture the periodic dynamics, demonstrating the network's ability to model non-linearities and chaotic dynamics. The addition of extra layers provides higher accuracy at minimal computational cost. These additional layers enable the network to expand its representational capacity, improving the prediction accuracy and reliability. The results demonstrate that HOSVD outperforms SVD in all tested scenarios, as evidenced by using different error metrics. Efficient mode truncation by HOSVD-based models enables the capture of complex temporal patterns, offering reliable predictions even in challenging, noise-influenced data sets. The findings underscore the adaptability and robustness of HOSVD-LSTM architectures, offering a scalable framework for modeling fluid dynamics.
Physics-informed KAN PointNet: Deep learning for simultaneous solutions to inverse problems in incompressible flow on numerous irregular geometries
Authors: Ali Kashefi, Tapan Mukerji
Date: 2025-04-08 00:00:00
Tasks: Kolmogorov-Arnold Networks

Kolmogorov-Arnold Networks (KANs) have gained attention as an alternative to traditional multilayer perceptrons (MLPs) for deep learning applications in computational physics, particularly for solving inverse problems with sparse data, as exemplified by the physics-informed Kolmogorov-Arnold network (PIKAN). However, the capability of KANs to simultaneously solve inverse problems over multiple irregular geometries within a single training run remains unexplored. To address this gap, we introduce the physics-informed Kolmogorov-Arnold PointNet (PI-KAN-PointNet), in which shared KANs are integrated into the PointNet architecture to capture the geometric features of computational domains. The loss function comprises the squared residuals of the governing equations, computed via automatic differentiation, along with sparse observations and partially known boundary conditions. We construct shared KANs using Jacobi polynomials and investigate their performance by considering Jacobi polynomials of different degrees and types in terms of both computational cost and prediction accuracy. As a benchmark test case, we consider natural convection in a square enclosure with a cylinder, where the cylinder's shape varies across a dataset of 135 geometries. PI-KAN-PointNet offers two main advantages. First, it overcomes the limitation of current PIKANs, which are restricted to solving only a single computational domain per training run, thereby reducing computational costs. Second, when comparing the performance of PI-KAN-PointNet with that of the physics-informed PointNet using MLPs, we observe that, with approximately the same number of trainable parameters and comparable computational cost in terms of the number of epochs, training time per epoch, and memory usage, PI-KAN-PointNet yields more accurate predictions, particularly for values on unknown boundary conditions involving nonsmooth geometries.
Image registration of 2D optical thin sections in a 3D porous medium: Application to a Berea sandstone digital rock image
Authors: Jaehong Chung, Wei Cai, Tapan Mukerji
Date: 2025-04-09 00:00:00
Tasks: Image Registration, SSIM

This study proposes a systematic image registration approach to align 2D optical thin-section images within a 3D digital rock volume. Using template image matching with differential evolution optimization, we identify the most similar 2D plane in 3D. The method is validated on a synthetic porous medium, achieving exact registration, and applied to Berea sandstone, where it achieves a structural similarity index (SSIM) of 0.990. With the registered images, we explore upscaling properties based on paired multimodal images, focusing on pore characteristics and effective elastic moduli. The thin-section image reveals 50 % more porosity and submicron pores than the registered CT plane. In addition, bulk and shear moduli from thin sections are 25 % and 30 % lower, respectively, than those derived from CT images. Beyond numerical comparisons, thin sections provide additional geological insights, including cementation, mineral phases, and weathering effects, which are not clear in CT images. This study demonstrates the potential of multimodal image registration to improve computed rock properties in digital rock physics by integrating complementary imaging modalities.
LOGLO-FNO: Efficient Learning of Local and Global Features in Fourier Neural Operators
Authors: Marimuthu Kalimuthu, David Holzmüller, Mathias Niepert
Date: 2025-04-05 00:00:00

Modeling high-frequency information is a critical challenge in scientific machine learning. For instance, fully turbulent flow simulations of Navier-Stokes equations at Reynolds numbers 3500 and above can generate high-frequency signals due to swirling fluid motions caused by eddies and vortices. Faithfully modeling such signals using neural networks depends on accurately reconstructing moderate to high frequencies. However, it has been well known that deep neural nets exhibit the so-called spectral bias toward learning low-frequency components. Meanwhile, Fourier Neural Operators (FNOs) have emerged as a popular class of data-driven models in recent years for solving Partial Differential Equations (PDEs) and for surrogate modeling in general. Although impressive results have been achieved on several PDE benchmark problems, FNOs often perform poorly in learning non-dominant frequencies characterized by local features. This limitation stems from the spectral bias inherent in neural networks and the explicit exclusion of high-frequency modes in FNOs and their variants. Therefore, to mitigate these issues and improve FNO's spectral learning capabilities to represent a broad range of frequency components, we propose two key architectural enhancements: (i) a parallel branch performing local spectral convolutions (ii) a high-frequency propagation module. Moreover, we propose a novel frequency-sensitive loss term based on radially binned spectral errors. This introduction of a parallel branch for local convolutions reduces number of trainable parameters by up to 50% while achieving the accuracy of baseline FNO that relies solely on global convolutions. Experiments on three challenging PDE problems in fluid mechanics and biological pattern formation, and the qualitative and spectral analysis of predictions show the effectiveness of our method over the state-of-the-art neural operator baselines.
Focal Cortical Dysplasia Type II Detection Using Cross Modality Transfer Learning and Grad-CAM in 3D-CNNs for MRI Analysis
Authors: Lorenzo Lasagni, Antonio Ciccarone, Renzo Guerrini, Matteo Lenge, Ludovico D'incerti
Date: 2025-04-10 00:00:00
Tasks: Explainable artificial intelligence, Explainable Artificial Intelligence (XAI), Transfer Learning

Focal cortical dysplasia (FCD) type II is a major cause of drug-resistant epilepsy, often curable only by surgery. Despite its clinical importance, the diagnosis of FCD is very difficult in MRI because of subtle abnormalities, leading to misdiagnosis. This study investigates the use of 3D convolutional neural networks (3D-CNNs) for FCD detection, using a dataset of 170 subjects (85 FCD patients and 85 controls) composed of T1-weighted and FLAIR MRI scans. In particular, it investigates the benefits obtained from cross-modality transfer learning and explainable artificial intelligence (XAI) techniques, in particular Gradient-weighted Class Activation Mapping (Grad-CAM). ResNet architectures (ResNet-18, -34, and -50) were implemented, employing transfer learning strategies that used pre-trained weights from segmentation tasks. Results indicate that transfer learning significantly enhances classification accuracy (up to 80.3%) and interpretability, as measured by a novel Heat-Score metric, which evaluates the model's focus on clinically relevant regions. Improvements in the Heat-Score metric underscore the model's seizure zone localization capabilities, bringing AI predictions and clinical insights closer together. These results highlight the importance of transfer learning, including cross-modality, and XAI in advancing AI-based medical diagnostics, especially for difficult-to-diagnose pathologies such as FCD.
AI analysis of medical images at scale as a health disparities probe: a feasibility demonstration using chest radiographs
Authors: Heather M. Whitney, Hui Li, Karen Drukker, Elbert Huang, Maryellen L. Giger
Date: 2025-04-08 00:00:00

Health disparities (differences in non-genetic conditions that influence health) can be associated with differences in burden of disease by groups within a population. Social determinants of health (SDOH) are domains such as health care access, dietary access, and economics frequently studied for potential association with health disparities. Evaluating SDOH-related phenotypes using routine medical images as data sources may enhance health disparities research. We developed a pipeline for using quantitative measures automatically extracted from medical images as inputs into health disparities index calculations. Our study focused on the use case of two SDOH demographic correlates (sex and race) and data extracted from chest radiographs of 1,571 unique patients. The likelihood of severe disease within the lung parenchyma from each image type, measured using an established deep learning model, was merged into a single numerical image-based phenotype for each patient. Patients were then separated into phenogroups by unsupervised clustering of the image-based phenotypes. The health rate for each phenogroup was defined as the median image-based phenotype for each SDOH used as inputs to four imaging-derived health disparities indices (iHDIs): one absolute measure (between-group variance) and three relative measures (index of disparity, Theil index, and mean log deviation). The iHDI measures demonstrated feasible values for each SDOH demographic correlate, showing potential for medical images to serve as a novel probe for health disparities. Large-scale AI analysis of medical images can serve as a probe for a novel data source for health disparities research.
When More Is Less: Higher Magnetic Fields and Their Limited Impact on SNR per Time Unit of Acquisition Time in Single Voxel Spectroscopy
Authors: Guodong Weng, Johannes Slotboom
Date: 2025-03-31 00:00:00
Tasks: Diagnostic

Magnetic resonance spectroscopy (MRS) offers significant diagnostic potential but is inherently constrained by low signal-to-noise ratio (SNR). While increasing the main magnetic field strength B_0 is theoretically linked to increased SNR, practically obtained gains in SNR from B07/4 to B0 depending on the domination of thermal noise at high B0, are not always realized. Especially in clinical settings the maximum reachable SNR is further constrained by the total available acquisition time (TA) and the regulatory limits on maximum tolerable specific absorption rate (SAR). This work attempts to derive mathematical expressions that enable systematical analysis of the theoretical achievable SNR-gain. One important notion is this context is the SNR gain per unit of measurement time as a function of the main magnetic field B0 strengths in the case of single voxel spectroscopy (SVS) pulse sequences. Our findings indicate that under given fixed total amount of (patient acceptable) measurement time TA, and maximum tolerable SAR limitation, together with conditions that ensure the adiabaticity of specific sequences, further increasing B0 does not further improve SNR per unit of measurement time. Key factors were identified, including RF-pulse bandwidth scaling with B0 and longitudinal relaxation time (T1) dependencies, impact the net gain as well. Our theoretical analysis emphasizes critical considerations for optimizing SNR per unit time in clinical MRS, even challenging the presumption that higher magnetic fields B0 always yield improved SNR per time unit of measurement time performance.
Exploiting individual differences to bootstrap communication
Authors: Richard A. Blythe, Casimir Fisch
Date: 2025-04-07 00:00:00

Establishing a communication system is hard because the intended meaning of a signal is unknown to its receiver when first produced, and the signaller also has no idea how that signal will be interpreted. Most theoretical accounts of the emergence of communication systems rely on feedback to reinforce behaviours that have led to successful communication in the past. However, providing such feedback requires already being able to communicate the meaning that was intended or interpreted. Therefore these accounts cannot explain how communication can be bootstrapped from non-communicative behaviours. Here we present a model that shows how a communication system, capable of expressing an unbounded number of meanings, can emerge as a result of individual behavioural differences in a large population without any pre-existing means to determine communicative success. The two key cognitive capabilities responsible for this outcome are behaving predictably in a given situation, and an alignment of psychological states ahead of signal production that derives from shared intentionality. Since both capabilities can exist independently of communication, our results are compatible with theories in which large flexible socially-learned communication systems like language are the product of a general but well-developed capacity for social cognition.
Unifying Physics- and Data-Driven Modeling via Novel Causal Spatiotemporal Graph Neural Network for Interpretable Epidemic Forecasting
Authors: Shuai Han, Lukas Stelz, Thomas R. Sokolowski, Kai Zhou, Horst Stöcker
Date: 2025-04-07 00:00:00
Tasks: Graph Neural Network

Accurate epidemic forecasting is crucial for effective disease control and prevention. Traditional compartmental models often struggle to estimate temporally and spatially varying epidemiological parameters, while deep learning models typically overlook disease transmission dynamics and lack interpretability in the epidemiological context. To address these limitations, we propose a novel Causal Spatiotemporal Graph Neural Network (CSTGNN), a hybrid framework that integrates a Spatio-Contact SIR model with Graph Neural Networks (GNNs) to capture the spatiotemporal propagation of epidemics. Inter-regional human mobility exhibits continuous and smooth spatiotemporal patterns, leading to adjacent graph structures that share underlying mobility dynamics. To model these dynamics, we employ an adaptive static connectivity graph to represent the stable components of human mobility and utilize a temporal dynamics model to capture fluctuations within these patterns. By integrating the adaptive static connectivity graph with the temporal dynamics graph, we construct a dynamic graph that encapsulates the comprehensive properties of human mobility networks. Additionally, to capture temporal trends and variations in infectious disease spread, we introduce a temporal decomposition model to handle temporal dependence. This model is then integrated with a dynamic graph convolutional network for epidemic forecasting. We validate our model using real-world datasets at the provincial level in China and the state level in Germany. Extensive studies demonstrate that our method effectively models the spatiotemporal dynamics of infectious diseases, providing a valuable tool for forecasting and intervention strategies. Furthermore, analysis of the learned parameters offers insights into disease transmission mechanisms, enhancing the interpretability and practical applicability of our model.
A Self-Supervised Framework for Space Object Behaviour Characterisation
Authors: Ian Groves, Andrew Campbell, James Fernandes, Diego Ramírez Rodríguez, Paul Murray, Massimiliano Vasile, Victoria Nockles
Date: 2025-04-08 00:00:00
Tasks: Anomaly Detection, Earth Observation, motion prediction, Object, Prediction, Self-Supervised Learning, Synthetic Data Generation

Foundation Models, pre-trained on large unlabelled datasets before task-specific fine-tuning, are increasingly being applied to specialised domains. Recent examples include ClimaX for climate and Clay for satellite Earth observation, but a Foundation Model for Space Object Behavioural Analysis has not yet been developed. As orbital populations grow, automated methods for characterising space object behaviour are crucial for space safety. We present a Space Safety and Sustainability Foundation Model focusing on space object behavioural analysis using light curves (LCs). We implemented a Perceiver-Variational Autoencoder (VAE) architecture, pre-trained with self-supervised reconstruction and masked reconstruction on 227,000 LCs from the MMT-9 observatory. The VAE enables anomaly detection, motion prediction, and LC generation. We fine-tuned the model for anomaly detection & motion prediction using two independent LC simulators (CASSANDRA and GRIAL respectively), using CAD models of boxwing, Sentinel-3, SMOS, and Starlink platforms. Our pre-trained model achieved a reconstruction error of 0.01%, identifying potentially anomalous light curves through reconstruction difficulty. After fine-tuning, the model scored 88% and 82% accuracy, with 0.90 and 0.95 ROC AUC scores respectively in both anomaly detection and motion mode prediction (sun-pointing, spin, etc.). Analysis of high-confidence anomaly predictions on real data revealed distinct patterns including characteristic object profiles and satellite glinting. Here, we demonstrate how self-supervised learning can simultaneously enable anomaly detection, motion prediction, and synthetic data generation from rich representations learned in pre-training. Our work therefore supports space safety and sustainability through automated monitoring and simulation capabilities.
Evaluating Parameter-Based Training Performance of Neural Networks and Variational Quantum Circuits
Authors: Michael Kölle, Alexander Feist, Jonas Stein, Sebastian Wölckert, Claudia Linnhoff-Popien
Date: 2025-04-09 00:00:00

In recent years, neural networks (NNs) have driven significant advances in machine learning. However, as tasks grow more complex, NNs often require large numbers of trainable parameters, which increases computational and energy demands. Variational quantum circuits (VQCs) offer a promising alternative: they leverage quantum mechanics to capture intricate relationships and typically need fewer parameters. In this work, we evaluate NNs and VQCs on simple supervised and reinforcement learning tasks, examining models with different parameter sizes. We simulate VQCs and execute selected parts of the training process on real quantum hardware to approximate actual training times. Our results show that VQCs can match NNs in performance while using significantly fewer parameters, despite longer training durations. As quantum technology and algorithms advance, and VQC architectures improve, we posit that VQCs could become advantageous for certain machine learning tasks.
Assumption-free fidelity bounds for hardware noise characterization
Authors: Nicolo Colombo
Date: 2025-04-09 00:00:00
Tasks: Conformal Prediction, Uncertainty Quantification, valid

In the Quantum Supremacy regime, quantum computers may overcome classical machines on several tasks if we can estimate, mitigate, or correct unavoidable hardware noise. Estimating the error requires classical simulations, which become unfeasible in the Quantum Supremacy regime. We leverage Machine Learning data-driven approaches and Conformal Prediction, a Machine Learning uncertainty quantification tool known for its mild assumptions and finite-sample validity, to find theoretically valid upper bounds of the fidelity between noiseless and noisy outputs of quantum devices. Under reasonable extrapolation assumptions, the proposed scheme applies to any Quantum Computing hardware, does not require modeling the device's noise sources, and can be used when classical simulations are unavailable, e.g. in the Quantum Supremacy regime.
Quantum neural networks facilitating quantum state classification
Authors: Diksha Sharma, Vivek Balasaheb Sabale, Thirumalai M., Atul Kumar
Date: 2025-04-09 00:00:00
Tasks: Classification, Multi-class Classification

The classification of quantum states into distinct classes poses a significant challenge. In this study, we address this problem using quantum neural networks in combination with a problem-inspired circuit and customised as well as predefined ans\"{a}tz. To facilitate the resource-efficient quantum state classification, we construct the dataset of quantum states using the proposed problem-inspired circuit. The problem-inspired circuit incorporates two-qubit parameterised unitary gates of varying entangling power, which is further integrated with the ans\"{a}tz, developing an entire quantum neural network. To demonstrate the capability of the selected ans\"{a}tz, we visualise the mitigated barren plateaus. The designed quantum neural network demonstrates the efficiency in binary and multi-class classification tasks. This work establishes a foundation for the classification of multi-qubit quantum states and offers the potential for generalisation to multi-qubit pure quantum states.