pwc-archive/papers-with-abstracts huggingface dataset:
Machine Learning Models for Reinforced Concrete Pipes Condition Prediction: The State-of-the-Art Using Artificial Neural Networks and Multiple Linear Regression in a Wisconsin Case Study
Authors: Mohsen Mohammadagha, Mohammad Najafi, Vinayak Kaushal, Ahmad Mahmoud Ahmad Jibreen
Date: 2025-02-01 00:00:00
Tasks: Transfer Learning

The aging sewer infrastructure in the U.S., covering 2.1 million kilometers, encounters increasing structural issues, resulting in around 75,000 yearly sanitary sewer overflows that present serious economic, environmental, and public health hazards. Conventional inspection techniques and deterministic models do not account for the unpredictable nature of sewer decline, whereas probabilistic methods depend on extensive historical data, which is frequently lacking or incomplete. This research intends to enhance predictive accuracy for the condition of sewer pipelines through machine learning models artificial neural networks (ANNs) and multiple linear regression (MLR) by integrating factors such as pipe age, material, diameter, environmental influences, and PACP ratings. ANNs utilized ReLU activation functions and Adam optimization, whereas MLR applied regularization to address multicollinearity, with both models assessed through metrics like RMSE, MAE, and R2. The findings indicated that ANNs surpassed MLR, attaining an R2 of 0.9066 compared to MLRs 0.8474, successfully modeling nonlinear relationships while preserving generalization. MLR, on the other hand, offered enhanced interpretability by pinpointing significant predictors such as residual buildup. As a result, pipeline degradation is driven by pipe length, age, and pipe diameter as key predictors, while depth, soil type, and segment show minimal influence in this analysis. Future studies ought to prioritize hybrid models that merge the accuracy of ANNs with the interpretability of MLR, incorporating advanced methods such as SHAP analysis and transfer learning to improve scalability in managing infrastructure and promoting environmental sustainability.
The number of immune defense and counter-defense systems sustained in the arms race between procaryotes and viruses
Authors: Yaroslav Ispolatov, Anna Lekontseva, Konstantin Severinov
Date: 2025-02-03 00:00:00
Tasks: Specificity

Prokaryotes have evolved various mechanisms to counter viruses, which in their turn developed numerous strategies to avoid defenses of the hosts. Dozens of such defense and counter-defense mechanisms have recently been discovered, yet the number of such systems held by a given virus or its host is limited. Here, we present numerical and theoretical arguments for the existence of the maximal number of ecologically and evolutionary sustainable defense and counter-defense systems maintained by both sides at any time of the never-ending evolutionary arms race. We find that the number of such systems is of the order of 10 for a broad range of assumptions about the costs and benefits of defense and counter-defense mechanisms and their specificity. This optimum in the number of defense and counter-defense systems appears as a result of a compromise between the metabolic and autoimmune costs of adding a new layer of defense and the benefits it conveys.
Generalized Lanczos method for systematic optimization of neural-network quantum states
Authors: Jia-Qi Wang, Rong-Qiang He, Zhong-Yi Lu
Date: 2025-02-03 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Variational Monte Carlo

Recently, artificial intelligence for science has made significant inroads into various fields of natural science research. In the field of quantum many-body computation, researchers have developed numerous ground state solvers based on neural-network quantum states (NQSs), achieving ground state energies with accuracy comparable to or surpassing traditional methods such as variational Monte Carlo methods, density matrix renormalization group, and quantum Monte Carlo methods. Here, we combine supervised learning, reinforcement learning, and the Lanczos method to develop a systematic approach to improving the NQSs of many-body systems, which we refer to as the NQS Lanczos method. The algorithm mainly consists of two parts: the supervised learning part and the reinforcement learning part. Through supervised learning, the Lanczos states are represented by the NQSs. Through reinforcement learning, the NQSs are further optimized. We analyze the reasons for the underfitting problem and demonstrate how the NQS Lanczos method systematically improves the energy in the highly frustrated regime of the two-dimensional Heisenberg $J_1$-$J_2$ model. Compared to the existing method that combines the Lanczos method with the restricted Boltzmann machine, the primary advantage of the NQS Lanczos method is its linearly increasing computational cost.
Predicting Steady-State Behavior in Complex Networks with Graph Neural Networks
Authors: Priodyuti Pradhan, Amit Reza
Date: 2025-02-02 00:00:00
Tasks: Graph Neural Network

In complex systems, information propagation can be defined as diffused or delocalized, weakly localized, and strongly localized. This study investigates the application of graph neural network models to learn the behavior of a linear dynamical system on networks. A graph convolution and attention-based neural network framework has been developed to identify the steady-state behavior of the linear dynamical system. We reveal that our trained model distinguishes the different states with high accuracy. Furthermore, we have evaluated model performance with real-world data. In addition, to understand the explainability of our model, we provide an analytical derivation for the forward and backward propagation of our framework.
OneForecast: A Universal Framework for Global and Regional Weather Forecasting
Authors: Yuan Gao, Hao Wu, Ruiqi Shu, Huanshuo Dong, Fan Xu, Rui Ray Chen, Yibo Yan, Qingsong Wen, Xuming Hu, Kun Wang, Jiahao Wu, Qing Li, Hui Xiong, Xiaomeng Huang
Date: 2025-02-01 00:00:00
Tasks: Weather Forecasting

Accurate weather forecasts are important for disaster prevention, agricultural planning, etc. Traditional numerical weather prediction (NWP) methods offer physically interpretable high-accuracy predictions but are computationally expensive and fail to fully leverage rapidly growing historical data. In recent years, deep learning models have made significant progress in weather forecasting, but challenges remain, such as balancing global and regional high-resolution forecasts, excessive smoothing in extreme event predictions, and insufficient dynamic system modeling. To address these issues, this paper proposes a global-regional nested weather forecasting framework (OneForecast) based on graph neural networks. By combining a dynamic system perspective with multi-grid theory, we construct a multi-scale graph structure and densify the target region to capture local high-frequency features. We introduce an adaptive messaging mechanism, using dynamic gating units to deeply integrate node and edge features for more accurate extreme event forecasting. For high-resolution regional forecasts, we propose a neural nested grid method to mitigate boundary information loss. Experimental results show that OneForecast performs excellently across global to regional scales and short-term to long-term forecasts, especially in extreme event predictions. Codes link https://github.com/YuanGao-YG/OneForecast.
Predicting concentration levels of air pollutants by transfer learning and recurrent neural network
Authors: Iat Hang Fong, Tengyue Li, Simon Fong, Raymond K. Wong, Antonio J. Tallón-Ballesteros
Date: 2025-01-30 00:00:00
Tasks: Prediction, Transfer Learning

Air pollution (AP) poses a great threat to human health, and people are paying more attention than ever to its prediction. Accurate prediction of AP helps people to plan for their outdoor activities and aids protecting human health. In this paper, long-short term memory (LSTM) recurrent neural networks (RNNs) have been used to predict the future concentration of air pollutants (APS) in Macau. Additionally, meteorological data and data on the concentration of APS have been utilized. Moreover, in Macau, some air quality monitoring stations (AQMSs) have less observed data in quantity, and, at the same time, some AQMSs recorded less observed data of certain types of APS. Therefore, the transfer learning and pre-trained neural networks have been employed to assist AQMSs with less observed data to build a neural network with high prediction accuracy. The experimental sample covers a period longer than 12-year and includes daily measurements from several APS as well as other more classical meteorological values. Records from five stations, four out of them are AQMSs and the remaining one is an automatic weather station, have been prepared from the aforesaid period and eventually underwent to computational intelligence techniques to build and extract a prediction knowledge-based system. As shown by experimentation, LSTM RNNs initialized with transfer learning methods have higher prediction accuracy; it incurred shorter training time than randomly initialized recurrent neural networks.
On Overlap Ratio in Defocused Electron Ptychography
Authors: Amirafshar Moshtaghpour, Angus I. Kirkland
Date: 2025-02-02 00:00:00
Tasks: Retrieval

Four-dimensional Scanning Transmission Electron Microscopy (4D STEM) with data acquired using a defocused electron probe is a promising tool for characterising complex biological specimens and materials through a phase retrieval process known as Electron Ptychography (EP). The efficacy of 4D STEM acquisition and the resulting quality of EP reconstruction depends on the overlap ratio of adjacent illuminated areas. This paper demonstrates how the overlap ratio impacts the data redundancy and the quality of the EP reconstruction. We define two quantities as a function of the overlap ratio that are independent of both the object and the EP algorithm. Subsequently, we evaluate an EP algorithm for varying overlap ratios using simulated 4D STEM datasets. Notably, a 40% or greater overlap ratio yields stable, high-quality reconstructions.
Accurate Pocket Identification for Binding-Site-Agnostic Docking
Authors: Yaroslav Balytskyi, Inna Hubenko, Alina Balytska, Christopher V. Kelly
Date: 2025-02-04 00:00:00
Tasks: Blind Docking, Drug Design

Accurate identification of druggable pockets is essential for structure-based drug design. However, most pocket-identification algorithms prioritize their geometric properties over downstream docking performance. To address this limitation, we developed RAPID-Net, a pocket-finding algorithm for seamless integration with docking workflows. When guiding AutoDock Vina, RAPID-Net outperforms DiffBindFR on the PoseBusters benchmark and enables blind docking on large proteins that AlphaFold 3 cannot process as a whole. Furthermore, RAPID-Net surpasses PUResNet and Kalasanty in docking accuracy and pocket-ligand intersection rates across diverse datasets, including PoseBusters, Astex Diverse Set, BU48, and Coach420. When accuracy is evaluated as ``at least one correct pose in the ensemble'', RAPID-Net outperforms AlphaFold 3 on the PoseBusters benchmark, suggesting that our approach can be further improved with a suitable pose reweighting tool offering a cost-effective and competitive alternative to AlphaFold 3 for docking. Finally, using several therapeutically relevant examples, we demonstrate the ability of RAPID-Net to identify remote functional sites, highlighting its potential to facilitate the development of innovative therapeutics.
Human fields and their impact on brain waves A pilot study
Authors: Jesus Acosta-Elias, Santiago Mendez-Moreno, Omar Vital-Ochoa, Ricardo Espinosa-Tanguma
Date: 2025-02-04 00:00:00
Tasks: EEG

During brain function, groups of neurons fire synchronously. When these groups are large enough, the resulting electrical signals can be measured on the scalp using Electroencephalography (EEG). The amplitude of these signals can be significant depending on the size and synchronization of the neural activity. EEG waves exhibit distinct patterns based on the brain's state, such as whether it is asleep, awake, engaged in mental calculations, or performing other cognitive functions. Additionally, these patterns can be modified by external factors, such as transcranial magnetic stimulation (TMS). TMS involves bringing an antenna that generates variable electromagnetic fields close to specific areas of the skull to treat certain pathologies. Given that the human body naturally generates magnetic fields, a question arises: Can these fields influence the EEG by modulating neuronal function, causing a resonance effect, or through some unknown interaction? This study investigated whether approaching the palm of the hand to the top of the head (Intervention) could induce effects in the EEG. Power Spectral Density (PSD) was obtained for the 30 seconds preceding the intervention (PSD_pre) and the final 30 seconds of the intervention (PSD_last). The exact Wilcoxon signed-rank test suggests that the median of PSD_pre is greater than the median of PSD_last at the 95% confidence level (p-value = 0.004353). In contrast, in the control group, the test indicates that at the 95% confidence level (p-value = 0.7667), the median of PSD_pre is not greater than the median of PSD_last.
Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity
Authors: Erpai Luo, Xinran Wei, Lin Huang, Yunyang Li, Han Yang, Zaishuo Xia, Zun Wang, Chang Liu, Bin Shao, Jia Zhang
Date: 2025-02-03 00:00:00
Tasks: Computational chemistry, Prediction

Hamiltonian matrix prediction is pivotal in computational chemistry, serving as the foundation for determining a wide range of molecular properties. While SE(3) equivariant graph neural networks have achieved remarkable success in this domain, their substantial computational cost--driven by high-order tensor product (TP) operations--restricts their scalability to large molecular systems with extensive basis sets. To address this challenge, we introduce SPHNet, an efficient and scalable equivariant network, that incorporates adaptive SParsity into Hamiltonian prediction. SPHNet employs two innovative sparse gates to selectively constrain non-critical interaction combinations, significantly reducing tensor product computations while maintaining accuracy. To optimize the sparse representation, we develop a Three-phase Sparsity Scheduler, ensuring stable convergence and achieving high performance at sparsity rates of up to 70%. Extensive evaluations on QH9 and PubchemQH datasets demonstrate that SPHNet achieves state-of-the-art accuracy while providing up to a 7x speedup over existing models. Beyond Hamiltonian prediction, the proposed sparsification techniques also hold significant potential for improving the efficiency and scalability of other SE(3) equivariant networks, further broadening their applicability and impact. Our code can be found at https://github.com/microsoft/SPHNet.
Gradient Alignment in Physics-informed Neural Networks: A Second-Order Optimization Perspective
Authors: Sifan Wang, Ananyae Kumar Bhartari, Bowen Li, Paris Perdikaris
Date: 2025-02-02 00:00:00
Tasks: Multi-Task Learning

Multi-task learning through composite loss functions is fundamental to modern deep learning, yet optimizing competing objectives remains challenging. We present new theoretical and practical approaches for addressing directional conflicts between loss terms, demonstrating their effectiveness in physics-informed neural networks (PINNs) where such conflicts are particularly challenging to resolve. Through theoretical analysis, we demonstrate how these conflicts limit first-order methods and show that second-order optimization naturally resolves them through implicit gradient alignment. We prove that SOAP, a recently proposed quasi-Newton method, efficiently approximates the Hessian preconditioner, enabling breakthrough performance in PINNs: state-of-the-art results on 10 challenging PDE benchmarks, including the first successful application to turbulent flows with Reynolds numbers up to 10,000, with 2-10x accuracy improvements over existing methods. We also introduce a novel gradient alignment score that generalizes cosine similarity to multiple gradients, providing a practical tool for analyzing optimization dynamics. Our findings establish frameworks for understanding and resolving gradient conflicts, with broad implications for optimization beyond scientific computing.
MetaOpenFOAM 2.0: Large Language Model Driven Chain of Thought for Automating CFD Simulation and Post-Processing
Authors: Yuxuan Chen, Xu Zhu, Hua Zhou, Zhuyin Ren
Date: 2025-02-01 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Computational Fluid Dynamics (CFD) is widely used in aerospace, energy, and biology to model fluid flow, heat transfer, and chemical reactions. While Large Language Models (LLMs) have transformed various domains, their application in CFD remains limited, particularly for complex tasks like post-processing. To bridge this gap, we introduce MetaOpenFOAM 2.0, which leverages Chain of Thought (COT) decomposition and iterative verification to enhance accessibility for non-expert users through natural language inputs. Tested on a new benchmark covering simulation (fluid flow, heat transfer, combustion) and post-processing (extraction, visualization), MetaOpenFOAM 2.0 achieved an Executability score of 6.3/7 and a pass rate of 86.9%, significantly outperforming MetaOpenFOAM 1.0 (2.1/7, 0%). Additionally, it proved cost-efficient, averaging $0.15 per case. An ablation study confirmed that COT-driven decomposition and iterative refinement substantially improved task performance. Furthermore, scaling laws showed that increasing COT steps enhanced accuracy while raising token usage, aligning with LLM post-training scaling trends. These results highlight the transformative potential of LLMs in automating CFD workflows for industrial and research applications. Code is available at https://github.com/Terry-cyx/MetaOpenFOAM
Generalized Lie Symmetries in Physics-Informed Neural Operators
Authors: Amy Xiang Wang, Zakhar Shumaylov, Peter Zaika, Ferdia Sherry, Carola-Bibiane Schönlieb
Date: 2025-02-01 00:00:00

Physics-informed neural operators (PINOs) have emerged as powerful tools for learning solution operators of partial differential equations (PDEs). Recent research has demonstrated that incorporating Lie point symmetry information can significantly enhance the training efficiency of PINOs, primarily through techniques like data, architecture, and loss augmentation. In this work, we focus on the latter, highlighting that point symmetries oftentimes result in no training signal, limiting their effectiveness in many problems. To address this, we propose a novel loss augmentation strategy that leverages evolutionary representatives of point symmetries, a specific class of generalized symmetries of the underlying PDE. These generalized symmetries provide a richer set of generators compared to standard symmetries, leading to a more informative training signal. We demonstrate that leveraging evolutionary representatives enhances the performance of neural operators, resulting in improved data efficiency and accuracy during training.
Physics-Informed Neural Network based Damage Identification for Truss Railroad Bridges
Authors: Althaf Shajihan, Kirill Mechitov, Girish Chowdhary, Billie F. Spencer Jr
Date: 2025-01-31 00:00:00

Railroad bridges are a crucial component of the U.S. freight rail system, which moves over 40 percent of the nation's freight and plays a critical role in the economy. However, aging bridge infrastructure and increasing train traffic pose significant safety hazards and risk service disruptions. The U.S. rail network includes over 100,000 railroad bridges, averaging one every 1.4 miles of track, with steel bridges comprising over 50% of the network's total bridge length. Early identification and assessment of damage in these bridges remain challenging tasks. This study proposes a physics-informed neural network (PINN) based approach for damage identification in steel truss railroad bridges. The proposed approach employs an unsupervised learning approach, eliminating the need for large datasets typically required by supervised methods. The approach utilizes train wheel load data and bridge response during train crossing events as inputs for damage identification. The PINN model explicitly incorporates the governing differential equations of the linear time-varying (LTV) bridge-train system. Herein, this model employs a recurrent neural network (RNN) based architecture incorporating a custom Runge-Kutta (RK) integrator cell, designed for gradient-based learning. The proposed approach updates the bridge finite element model while also quantifying damage severity and localizing the affected structural members. A case study on the Calumet Bridge in Chicago, Illinois, with simulated damage scenarios, is used to demonstrate the model's effectiveness in identifying damage while maintaining low false-positive rates. Furthermore, the damage identification pipeline is designed to seamlessly integrate prior knowledge from inspections and drone surveys, also enabling context-aware updating and assessment of bridge's condition.
Binned Spectral Power Loss for Improved Prediction of Chaotic Systems
Authors: Dibyajyoti Chakraborty, Arvind T. Mohan, Romit Maulik
Date: 2025-02-01 00:00:00
Tasks: Deep Learning, Time Series Forecasting

Forecasting multiscale chaotic dynamical systems with deep learning remains a formidable challenge due to the spectral bias of neural networks, which hinders the accurate representation of fine-scale structures in long-term predictions. This issue is exacerbated when models are deployed autoregressively, leading to compounding errors and instability. In this work, we introduce a novel approach to mitigate the spectral bias which we call the Binned Spectral Power (BSP) Loss. The BSP loss is a frequency-domain loss function that adaptively weighs errors in predicting both larger and smaller scales of the dataset. Unlike traditional losses that focus on pointwise misfits, our BSP loss explicitly penalizes deviations in the energy distribution across different scales, promoting stable and physically consistent predictions. We demonstrate that the BSP loss mitigates the well-known problem of spectral bias in deep learning. We further validate our approach for the data-driven high-dimensional time-series forecasting of a range of benchmark chaotic systems which are typically intractable due to spectral bias. Our results demonstrate that the BSP loss significantly improves the stability and spectral accuracy of neural forecasting models without requiring architectural modifications. By directly targeting spectral consistency, our approach paves the way for more robust deep learning models for long-term forecasting of chaotic dynamical systems.