pwc-archive/papers-with-abstracts huggingface dataset:
Benchmarking machine learning models for predicting aerofoil performance
Authors: Oliver Summerell, Gerardo Aragon-Camarasa, Stephanie Ordonez Sanchez
Date: 2025-04-22 00:00:00
Tasks: Benchmarking

This paper investigates the capability of Neural Networks (NNs) as alternatives to the traditional methods to analyse the performance of aerofoils used in the wind and tidal energy industry. The current methods used to assess the characteristic lift and drag coefficients include Computational Fluid Dynamics (CFD), thin aerofoil and panel methods, all face trade-offs between computational speed and the accuracy of the results and as such NNs have been investigated as an alternative with the aim that it would perform both quickly and accurately. As such, this paper provides a benchmark for the windAI_bench dataset published by the National Renewable Energy Laboratory (NREL) in the USA. In order to validate the methodology of the benchmarking, the AirfRANS {\tt arXiv:2212.07564v3} dataset is used as both a starting point and a point of comparison. This study evaluates four neural networks (MLP, PointNet, GraphSAGE, GUNet) trained on a range aerofoils at 25 angles of attack (4$^\circ$ to 20$^\circ$). to predict fluid flow and calculate lift coefficients ($C_L$) via the panel method. GraphSAGE and GUNet performed well during the testing phase, but underperformed during validation. Accordingly, this paper has identified PointNet and MLP as the two strongest models tested, however whilst the results from MLP are more commonly correct for predicting the behaviour of the fluid, the results from PointNet provide the more accurate results for calculating $C_L$.
LBM-GNN: Graph Neural Network Enhanced Lattice Boltzmann Method
Authors: Yue Li
Date: 2025-04-20 00:00:00
Tasks: Graph Neural Network

In this paper, we present LBM-GNN, a novel approach that enhances the traditional Lattice Boltzmann Method (LBM) with Graph Neural Networks (GNNs). We apply this method to fluid dynamics simulations, demonstrating improved stability and accuracy compared to standard LBM implementations. The method is validated using benchmark problems such as the Taylor-Green vortex, focusing on accuracy, conservation properties, and performance across different Reynolds numbers and grid resolutions. Our results indicate that GNN-enhanced LBM can maintain better conservation properties while improving numerical stability at higher Reynolds numbers.
Data Assimilation-based Simultaneous Phase-Resolved Ocean Wave and Ship Motion Forecast
Authors: Guangyao Wang, Yulin Pan
Date: 2025-04-16 00:00:00

This paper presents a data-assimilation (DA)-based approach to forecast the phase-resolved wave evolution process and ship motion, which is developed by coupling the high-order spectral method (HOS), ensemble Kalman filter (EnKF), and a Cummins-equation-based ship model (CMI). With the developed EnKF-HOS-CMI method, the observation data for wave, ship, or both can be incorporated into the model, therefore producing the optimal analysis results. The developed method is validated and tested based on a synthetic problem on the motions of an irregular wave field and a box-shaped free-floating ship. We show that the EnKF-HOS-CMI method achieves much higher accuracy in the long-term simulation of nonlinear phase-resolved wave field and ship motion in comparison with the HOS-CMI method. Also, the ship parameters are estimated accurately by using a parameter-augmented state space in EnKF.
On the workflow, opportunities and challenges of developing foundation model in geophysics
Authors: Hanlin Sheng, Xinming Wu, Hang Gao, Haibin Di, Sergey Fomel, Jintao Li, Xu Si
Date: 2025-04-24 00:00:00
Tasks: Computational Efficiency, Geophysics, Transfer Learning

Foundation models, as a mainstream technology in artificial intelligence, have demonstrated immense potential across various domains in recent years, particularly in handling complex tasks and multimodal data. In the field of geophysics, although the application of foundation models is gradually expanding, there is currently a lack of comprehensive reviews discussing the full workflow of integrating foundation models with geophysical data. To address this gap, this paper presents a complete framework that systematically explores the entire process of developing foundation models in conjunction with geophysical data. From data collection and preprocessing to model architecture selection, pre-training strategies, and model deployment, we provide a detailed analysis of the key techniques and methodologies at each stage. In particular, considering the diversity, complexity, and physical consistency constraints of geophysical data, we discuss targeted solutions to address these challenges. Furthermore, we discuss how to leverage the transfer learning capabilities of foundation models to reduce reliance on labeled data, enhance computational efficiency, and incorporate physical constraints into model training, thereby improving physical consistency and interpretability. Through a comprehensive summary and analysis of the current technological landscape, this paper not only fills the gap in the geophysics domain regarding a full-process review of foundation models but also offers valuable practical guidance for their application in geophysical data analysis, driving innovation and advancement in the field.
HydroStartML: A combined machine learning and physics-based approach to reduce hydrological model spin-up time
Authors: Louisa Pawusch, Stefania Scheurer, Wolfgang Nowak, Reed Maxwell
Date: 2025-04-24 00:00:00

Finding the initial depth-to-water table (DTWT) configuration of a catchment is a critical challenge when simulating the hydrological cycle with integrated models, significantly impacting simulation outcomes. Traditionally, this involves iterative spin-up computations, where the model runs under constant atmospheric settings until steady-state is achieved. These so-called model spin-ups are computationally expensive, often requiring many years of simulated time, particularly when the initial DTWT configuration is far from steady state. To accelerate the model spin-up process we developed HydroStartML, a machine learning emulator trained on steady-state DTWT configurations across the contiguous United States. HydroStartML predicts, based on available data like conductivity and surface slopes, a DTWT configuration of the respective watershed, which can be used as an initial DTWT. Our results show that initializing spin-up computations with HydroStartML predictions leads to faster convergence than with other initial configurations like spatially constant DTWTs. The emulator accurately predicts configurations close to steady state, even for terrain configurations not seen in training, and allows especially significant reductions in computational spin-up effort in regions with deep DTWTs. This work opens the door for hybrid approaches that blend machine learning and traditional simulation, enhancing predictive accuracy and efficiency in hydrology for improving water resource management and understanding complex environmental interactions.
Dargana: fine-tuning EarthPT for dynamic tree canopy mapping from space
Authors: Michael J. Smith, Luke Fleming, James E. Geach, Ryan J. Roberts, Freddie Kalaitzis, James Banister
Date: 2025-04-24 00:00:00
Tasks: Management

We present Dargana, a fine-tuned variant of the EarthPT time-series foundation model that achieves specialisation using <3% of its pre-training data volume and 5% of its pre-training compute. Dargana is fine-tuned to generate regularly updated classification of tree canopy cover at 10m resolution, distinguishing conifer and broadleaved tree types. Using Cornwall, UK, as a test case, the model achieves a pixel-level ROC-AUC of 0.98 and a PR-AUC of 0.83 on unseen satellite imagery. Dargana can identify fine structures like hedgerows and coppice below the training sample limit, and can track temporal changes to canopy cover such as new woodland establishment. Our results demonstrate how pre-trained Large Observation Models like EarthPT can be specialised for granular, dynamic land cover monitoring from space, providing a valuable, scalable tool for natural capital management and conservation.
Full waveform inversion with CNN-based velocity representation extension
Authors: Xinru Mu, Omar M. Saad, Tariq Alkhalifah
Date: 2025-04-22 00:00:00
Tasks: Self-Supervised Learning

Full waveform inversion (FWI) updates the velocity model by minimizing the discrepancy between observed and simulated data. However, discretization errors in numerical modeling and incomplete seismic data acquisition can introduce noise, which propagates through the adjoint operator and affects the accuracy of the velocity gradient, thereby impacting the FWI inversion accuracy. To mitigate the influence of noise on the gradient, we employ a convolutional neural network (CNN) to refine the velocity model before performing the forward simulation, aiming to reduce noise and provide a more accurate velocity update direction. We use the same data misfit loss to update both the velocity and network parameters, thereby forming a self-supervised learning procedure. We propose two implementation schemes, which differ in whether the velocity update passes through the CNN. In both methodologies, the velocity representation is extended (VRE) by using a neural network in addition to the grid-based velocities. Thus, we refer to this general approach as VRE-FWI. Synthetic and real data tests demonstrate that the proposed VRE-FWI achieves higher velocity inversion accuracy compared to traditional FWI, at a marginal additional computational cost of approximately 1%.
Stabilizing 3D EPI time series by servo navigation and phase equalization exploiting repeated shots (PEERS)
Authors: Malte Riedel, Thomas Ulrich, Samuel Bianchi, Klaas P. Pruessmann
Date: 2025-05-06 00:00:00
Tasks: Time Series

Purpose: To enable run-time head motion control and robust frequency corrections for 3D EPI fMRI. Methods: A short 3D orbital navigator (3 ms) is inserted into a 3D EPI sequence. A linear perturbation model is calibrated to estimate rigid motion and frequency parameters per shot. Rigid motion is corrected by scan geometry updates in run-time, while several techniques are investigated to stabilize navigator-based frequency corrections in the reconstruction. An additional method termed PEERS is proposed that exploits the repetitive structure of fMRI scans to fine-tune shot-wise phase and frequency estimates using the motion-corrected EPI data itself. Results: Servo navigation effectively reduces motion in the raw data of in-vivo fMRI scans in six subjects. PEERS provides high-precision frequency parameters for robust phase-corrected reconstructions in the phantom and in-vivo accounting for scanner drifts and slice encoding-related effects on EPI. In combination, servo navigation and PEERS achieve successful intra-volume corrections and consistent tSNR improvements of 8% on average throughout the brain. The two methods prove to be highly synergetic. Conclusion: Servo navigation achieves high-precision motion correction for 3D-EPI fMRI in run-time and, in synergy with PEERS, provides stable frequency corrections with short navigators even for long echo times. With its automatic self-calibration and no hardware requirements, servo navigation and PEERS enable effective plug-and-play motion correction for 3D fMRI.
Physical foundations for trustworthy medical imaging: a review for artificial intelligence researchers
Authors: Miriam Cobo, David Corral Fontecha, Wilson Silva, Lara Lloret Iglesias
Date: 2025-04-28 00:00:00

Artificial intelligence in medical imaging has seen unprecedented growth in the last years, due to rapid advances in deep learning and computing resources. Applications cover the full range of existing medical imaging modalities, with unique characteristics driven by the physics of each technique. Yet, artificial intelligence professionals entering the field, and even experienced developers, often lack a comprehensive understanding of the physical principles underlying medical image acquisition, which hinders their ability to fully leverage its potential. The integration of physics knowledge into artificial intelligence algorithms enhances their trustworthiness and robustness in medical imaging, especially in scenarios with limited data availability. In this work, we review the fundamentals of physics in medical images and their impact on the latest advances in artificial intelligence, particularly, in generative models and reconstruction algorithms. Finally, we explore the integration of physics knowledge into physics-inspired machine learning models, which leverage physics-based constraints to enhance the learning of medical imaging features.
Machine Learning-Based Modeling of the Anode Heel Effect in X-ray Beam Monte Carlo Simulations
Authors: Hussein Harb, Didier Benoit, Axel Rannou, Chi-Hieu Pham, Valentin Tissot, Bahaa Nasr, Julien Bert
Date: 2025-04-27 00:00:00

This study enhances Monte Carlo simulation accuracy in X-ray imaging by developing an AI-driven model for the anode heel effect, achieving improved beam intensity distribution and dosimetric precision. Through dynamic adjustments to beam weights on the anode and cathode sides of the X-ray tube, our machine learning model effectively replicates the asymmetry characteristic of clinical X-ray beams. Experimental results reveal dose rate increases of up to 9.6% on the cathode side and reductions of up to 12.5% on the anode side, for energy levels between 50 and 120 kVp. These experimentally optimized beam weights were integrated into the OpenGATE and GGEMS Monte Carlo toolkits, significantly advancing dosimetric simulation accuracy and the image quality which closely resembles the clinical imaging. Validation with fluence and dose actors demonstrated that the AI-based model closely mirrors clinical beam behavior, providing substantial improvements in dose consistency and accuracy over conventional X-ray models. This approach provides a robust framework for improving X-ray dosimetry, with potential applications in dose optimization, imaging quality enhancement, and radiation safety in both clinical and research settings.
Anatomy-constrained modelling of image-derived input functions in dynamic PET using multi-organ segmentation
Authors: Valentin Langer, Kartikay Tehlan, Thomas Wendler
Date: 2025-04-23 00:00:00
Tasks: Anatomy, Organ Segmentation

Accurate kinetic analysis of [$^{18}$F]FDG distribution in dynamic positron emission tomography (PET) requires anatomically constrained modelling of image-derived input functions (IDIFs). Traditionally, IDIFs are obtained from the aorta, neglecting anatomical variations and complex vascular contributions. This study proposes a multi-organ segmentation-based approach that integrates IDIFs from the aorta, portal vein, pulmonary artery, and ureters. Using high-resolution CT segmentations of the liver, lungs, kidneys, and bladder, we incorporate organ-specific blood supply sources to improve kinetic modelling. Our method was evaluated on dynamic [$^{18}$F]FDG PET data from nine patients, resulting in a mean squared error (MSE) reduction of $13.39\%$ for the liver and $10.42\%$ for the lungs. These initial results highlight the potential of multiple IDIFs in improving anatomical modelling and fully leveraging dynamic PET imaging. This approach could facilitate the integration of tracer kinetic modelling into clinical routine.
Fluorescence Reference Target Quantitative Analysis Library
Authors: Eammon A. Littler, Emmanuel A. Mannoh, Ethan P. M. LaRochelle
Date: 2025-04-22 00:00:00
Tasks: Benchmarking

Standardized performance evaluation of fluorescence imaging systems remains a critical unmet need in the field of fluorescence-guided surgery (FGS). While the American Association of Physicists in Medicine (AAPM) TG311 report and recent FDA draft guidance provide recommended metrics for system characterization, practical tools for extracting these metrics remain limited, inconsistent, and often inaccessible. We present QUEL-QAL, an open-source Python library designed to streamline and standardize the quantitative analysis of fluorescence images using solid reference targets. The library provides a modular, reproducible workflow that includes region of interest (ROI) detection, statistical analysis, and visualization capabilities. QUEL-QAL supports key metrics such as response linearity, limit of detection, depth sensitivity, and spatial resolution, in alignment with regulatory and academic guidance. Built on widely adopted Python packages, the library is designed to be extensible, enabling users to adapt it to novel target designs and analysis protocols. By promoting transparency, reproducibility, and regulatory alignment, QUEL-QAL offers a foundational tool to support standardized benchmarking and accelerate the development and evaluation of fluorescence imaging systems.
Towards Robust and Generalizable Gerchberg Saxton based Physics Inspired Neural Networks for Computer Generated Holography: A Sensitivity Analysis Framework
Authors: Ankit Amrutkar, Björn Kampa, Volkmar Schulz, Johannes Stegmaier, Markus Rothermel, Dorit Merhof
Date: 2025-04-30 00:00:00
Tasks: Benchmarking, Learning Theory, Model Selection, Retrieval, Sensitivity

Computer-generated holography (CGH) enables applications in holographic augmented reality (AR), 3D displays, systems neuroscience, and optical trapping. The fundamental challenge in CGH is solving the inverse problem of phase retrieval from intensity measurements. Physics-inspired neural networks (PINNs), especially Gerchberg-Saxton-based PINNs (GS-PINNs), have advanced phase retrieval capabilities. However, their performance strongly depends on forward models (FMs) and their hyperparameters (FMHs), limiting generalization, complicating benchmarking, and hindering hardware optimization. We present a systematic sensitivity analysis framework based on Saltelli's extension of Sobol's method to quantify FMH impacts on GS-PINN performance. Our analysis demonstrates that SLM pixel-resolution is the primary factor affecting neural network sensitivity, followed by pixel-pitch, propagation distance, and wavelength. Free space propagation forward models demonstrate superior neural network performance compared to Fourier holography, providing enhanced parameterization and generalization. We introduce a composite evaluation metric combining performance consistency, generalization capability, and hyperparameter perturbation resilience, establishing a unified benchmarking standard across CGH configurations. Our research connects physics-inspired deep learning theory with practical CGH implementations through concrete guidelines for forward model selection, neural network architecture, and performance evaluation. Our contributions advance the development of robust, interpretable, and generalizable neural networks for diverse holographic applications, supporting evidence-based decisions in CGH research and implementation.
3D Deep-learning-based Segmentation of Human Skin Sweat Glands and Their 3D Morphological Response to Temperature Variations
Authors: Shaoyu Pei, Renxiong Wu, Hao Zheng, Lang Qin, Shuaichen Lin, Yuxing Gan, Wenjing Huang, Zhixuan Wang, Mohan Qin, Yong liu, Guangming Ni
Date: 2025-04-24 00:00:00
Tasks: Semantic Segmentation

Skin, the primary regulator of heat exchange, relies on sweat glands for thermoregulation. Alterations in sweat gland morphology play a crucial role in various pathological conditions and clinical diagnoses. Current methods for observing sweat gland morphology are limited by their two-dimensional, in vitro, and destructive nature, underscoring the urgent need for real-time, non-invasive, quantifiable technologies. We proposed a novel three-dimensional (3D) transformer-based multi-object segmentation framework, integrating a sliding window approach, joint spatial-channel attention mechanism, and architectural heterogeneity between shallow and deep layers. Our proposed network enables precise 3D sweat gland segmentation from skin volume data captured by optical coherence tomography (OCT). For the first time, subtle variations of sweat gland 3D morphology in response to temperature changes, have been visualized and quantified. Our approach establishes a benchmark for normal sweat gland morphology and provides a real-time, non-invasive tool for quantifying 3D structural parameters. This enables the study of individual variability and pathological changes in sweat gland structure, advancing dermatological research and clinical applications, including thermoregulation and bromhidrosis treatment.
Phase tomography with axial structured illumination
Authors: N Goyal, K Khare
Date: 2025-04-19 00:00:00

Holographic Tomography (HT) or Optical Diffraction Tomography (ODT) provides slice-by-slice information about the refractive index (RI) of three-dimensional (3D) samples and is emerging as an important label-free imaging modality for Life sciences. HT systems go beyond the digital holographic microscopy (DHM) systems that provide a two-dimensional (2D) representation of the total accumulated phase acquired by a plane beam on transmission through a 3D sample. While the early HT systems used a direct reconstruction methodology based on the Fourier diffraction theorem, in recent years, there has been an increasing shift towards using iterative optimization frameworks for solving the 3D RI reconstruction problem. Despite this algorithmic framework shift, the HT system hardware still largely uses the multi-angle illumination geometries that were suitable for reconstructions based on the Fourier diffraction theorem. The present work examines the possibility of HT reconstruction through the use of on-axis structured illumination(s) that nominally illuminates the 3D sample along the axial direction. Through a simulation study, it is shown that a cross-talk free slice-by-slice 3D RI reconstruction of the sample is possible in this case via the use of sparsity penalties if the slice-to-slice distance obeys a design curve based on the notion of effective depth of focus. The simulation results for two-, three- and four-slice 3D objects with laterally overlapping features clearly outline the separate roles played by the slice-to-slice de-correlation of the field propagating through the 3D sample and that of the sparsity penalty used to guide the iterative solution. Our results suggest the possibility of realizing an Axial Structured Illumination Tomography (ASIT) system configuration that avoids the use of hardware-intensive multi-angle illumination geometry.
DeepPD: Joint Phase and Object Estimation from Phase Diversity with Neural Calibration of a Deformable Mirror
Authors: Magdalena C. Schneider, Courtney Johnson, Cedric Allier, Larissa Heinrich, Diane Adjavon, Joren Husic, Patrick La Rivière, Stephan Saalfeld, Hari Shroff
Date: 2025-04-19 00:00:00
Tasks: Diversity, Object, Object Reconstruction

Sample-induced aberrations and optical imperfections limit the resolution of fluorescence microscopy. Phase diversity is a powerful technique that leverages complementary phase information in sequentially acquired images with deliberately introduced aberrations--the phase diversities--to enable phase and object reconstruction and restore diffraction-limited resolution. These phase diversities are typically introduced into the optical path via a deformable mirror. Existing phase-diversity-based methods are limited to Zernike modes, require large numbers of diversity images, or depend on accurate mirror calibration--which are all suboptimal. We present DeepPD, a deep learning-based framework that combines neural representations of the object and wavefront with a learned model of the deformable mirror to jointly estimate both object and phase from only five images. DeepPD improves robustness and reconstruction quality over previous approaches, even under severe aberrations. We demonstrate its performance on calibration targets and biological samples, including immunolabeled myosin in fixed PtK2 cells.