Frontiers in Applied Physics and Mathematics
Frontiers in Applied Physics and Mathematics is a peer-reviewed, open-access journal dedicated to publishing high-quality research in the fields of applied physics and mathematics. The journal aims to bridge the gap between theoretical advancements and practical applications, providing a platform for scientists, researchers, and academics to share their discoveries and innovations. With a focus on interdisciplinary studies, the journal covers a wide range of topics, including quantum mechanics, statistical physics, computational physics, materials science, applied mathematics, and mathematical modeling.
Physics-Informed Graph Neural Networks for Supply Chain Disruption Prediction and Mitigation
Global supply chains face unprecedented challenges from multi-modal disruptions including natural disasters, geopolitical tensions, and market volatility. Traditional data-driven approaches for disruption prediction often fail to capture the underlying physical constraints and causal relationships governing supply chain dynamics. This paper introduces a novel Physics-Informed Graph Neural Network (PI-GNN) framework that integrates domain knowledge from supply chain theory with graph-based deep learning architectures for enhanced disruption prediction and mitigation strategies. The proposed methodology embeds physical laws such as conservation of flow, capacity constraints, and lead time dependencies directly into the neural network training process through custom loss functions and architectural constraints. We demonstrate that by incorporating physics-based regularization terms derived from supply chain fundamentals, the PI-GNN achieves superior predictive performance compared to purely data-driven GNNs, particularly in scenarios with limited historical data. Experimental results on real-world supply chain networks show that the PI-GNN framework reduces prediction error by 23% for disruption events and provides interpretable insights for proactive mitigation strategies. The framework facilitates real-time risk assessment across multi-tier supply networks while maintaining computational efficiency suitable for large-scale deployments.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
Causal Discovery in Multi-Echelon Supply Networks: Leveraging Foundation Models for Demand Propagation Analysis
The complexity of modern multi-echelon supply networks presents significant challenges in understanding demand propagation patterns and causal relationships across network tiers. Traditional correlation-based approaches fail to capture the true causal mechanisms underlying supply chain disruptions and demand amplification phenomena. This research proposes a novel framework that integrates causal discovery methodologies with foundation models to analyze demand propagation in multi-echelon supply networks. By leveraging large-scale pre-trained models adapted for supply chain analytics, we develop a system capable of identifying causal relationships between demand signals, inventory decisions, and operational parameters across network echelons. The framework employs graph neural networks combined with causal inference algorithms to construct dynamic causal graphs that represent inter-echelon dependencies. Our approach addresses the limitations of existing methods by explicitly modeling directional causality rather than mere correlation, enabling more accurate root cause attribution and predictive capabilities. Empirical validation using supply chain simulation data demonstrates that network structural parameters significantly impact demand amplification, with high echelon configurations exhibiting peak amplification ratios exceeding fifty times baseline levels. The temporal evolution analysis reveals distinct propagation patterns across different network structures, validating the framework's ability to capture complex spatio-temporal dynamics. This research contributes to the emerging field of foundation models in supply chain management while advancing causal discovery techniques for complex network structures.
Evaluating the Incremental Value of E-commerce Promotional Campaigns: An Uplift Modeling Approach
E-commerce platforms frequently deploy promotional campaigns to stimulate customer purchases, yet accurately quantifying their true incremental impact remains challenging due to the presence of self-selection bias among customers. Traditional response models often fail to distinguish between customers who would purchase regardless of the campaign and those genuinely influenced by it. This study employs uplift modeling, a causal inference technique, to estimate the individual-level incremental effect of promotional campaigns on customer purchasing behavior. Using a real-world e-commerce dataset, we compare the performance of various uplift modeling algorithms, including the uplift random forest and causal forest, against conventional classification approaches. Our findings reveal that uplift models significantly outperform traditional methods in identifying persuadable customers—those whose purchase decisions are positively influenced by the campaign. Additionally, the results highlight the potential for cost savings by targeting only high-uplift segments, thereby avoiding wasteful spending on customers who are either immune or likely to purchase organically. This research underscores the strategic importance of uplift modeling in optimizing marketing resource allocation and enhancing return on investment for e-commerce promotions.
Copyright (c) 2025 Hongwei Wu, Yajing Zhu (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Predicting Consumer Confidence Index Using Social Media Sentiment Analysis
The Consumer Confidence Index (CCI) serves as a critical economic indicator, yet traditional survey-based methods for its measurement often entail delays and high costs. This study explores the potential of leveraging real-time social media data as an alternative approach to predict CCI trends. By employing sentiment analysis on a large dataset of user-generated content from platforms such as Twitter and Reddit, this research quantifies public sentiment and examines its correlation with official CCI values. A regression-based predictive model was developed, incorporating sentiment scores alongside macroeconomic variables for enhanced accuracy. The findings reveal a statistically significant relationship between aggregated social media sentiment and CCI movements, with the model demonstrating robust predictive performance, particularly in capturing short-term fluctuations. These results underscore the value of social media as a timely and cost-effective supplementary tool for forecasting consumer confidence. The study contributes to the growing body of literature on non-traditional data sources in economic forecasting and offers practical implications for policymakers and businesses seeking to anticipate economic trends.
Copyright (c) 2025 Yan Li, Xiaowei Wang , Jing Chen (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Parameter-Driven Efficiency Evaluation of Solar Thermal Collectors Using CFD–ML Integration
This research presents a comprehensive computational framework integrating Computational Fluid Dynamics (CFD) with Adaptive Neuro-Fuzzy Inference System (ANFIS) for parameter-driven efficiency evaluation of parabolic trough solar thermal collectors. The methodology combines high-fidelity CFD simulations with advanced ANFIS machine learning techniques to predict thermal performance parameters across diverse operational conditions. A validated CFD model was developed using ANSYS Fluent to simulate the complex thermal and fluid dynamic behavior within parabolic trough collector systems, generating 920 numerical datasets encompassing varying flow rates, inlet temperatures, solar irradiation levels, and geometric configurations. The ANFIS model demonstrated exceptional predictive capabilities, achieving coefficient of determination (R²) values of 0.94 for thermal efficiency predictions. The integrated CFD-ANFIS framework reduces computational time by 82% compared to traditional CFD approaches while maintaining prediction accuracy within 4.2% error bounds. Key findings indicate that solar irradiation intensity and heat transfer fluid mass flow rate are the most influential parameters affecting collector efficiency, with optimal performance achieved at Reynolds numbers between 10000-15000. The developed framework enables rapid parametric optimization and real-time performance assessment for parabolic trough solar thermal collector systems, providing significant advantages for design engineers and system operators in concentrating solar power applications.
Towards Sustainable Cold Storage: A Hybrid CFD–ML Approach to Temperature and Energy Optimization
This research presents an innovative hybrid computational framework that integrates Computational Fluid Dynamics (CFD) with Machine Learning (ML) algorithms to optimize temperature distribution and energy efficiency in cold storage facilities. The study addresses critical challenges in maintaining uniform thermal conditions while minimizing operational costs through advanced modeling techniques. A comprehensive CFD validation study demonstrates excellent agreement with established benchmarks, achieving correlation coefficients exceeding 0.95 for airflow distribution patterns. The hybrid approach employs systematic parameter analysis including surface velocity effects, temporal temperature variations, and multi-zone optimization strategies. Machine learning models trained on extensive CFD datasets achieve remarkable performance improvements, with temperature prediction accuracies reaching R² = 0.94 and energy consumption forecasting achieving R² = 0.91. Comparative analysis between optimized and conventional cold storage operations reveals significant improvements across multiple performance metrics. The optimized system demonstrates 23% reduction in energy consumption, 35% improvement in temperature uniformity, 28% decrease in product weight loss, and substantial reduction in transpiration rates. The framework successfully identifies optimal operational conditions including airflow velocities between 1.2-1.8 m/s and strategic evaporator positioning that enhances thermal performance. This integrated methodology provides a computationally efficient alternative to traditional approaches while maintaining high accuracy, enabling real-time optimization and intelligent control of cold storage systems.
Behavior Path Analysis for Blockchain Fraud Detection Using Graph Neural Architectures
With the deep penetration of blockchain technology across various fields, its security system faces severe challenges, and fraudulent activities are becoming increasingly frequent. This study focuses on the problem of fraud detection in blockchain and proposes an innovative model, FraudGNN, based on Graph Neural Networks (GNN). The model constructs a dynamic transaction graph, where transaction addresses are treated as nodes and asset transfer relationships as edges, incorporating time-series features. A Graph Attention Network (GAT) is used to extract behavioral features from node neighborhoods. In addition, a Bidirectional Long Short-Term Memory network (Bi-LSTM) is introduced to capture behavioral paths across block-level transactions, enabling accurate classification and prediction of abnormal accounts within blockchain networks. Experiments conducted on an Ethereum transaction dataset—containing approximately 3.6 million transaction records and 40,000 labeled addresses—show that the FraudGNN model significantly outperforms traditional methods such as Random Forest and Graph Convolutional Networks (GCN) in key metrics, achieving 91.2% precision, 87.5% recall, and an F1-score of 89.3%. In particular, the model demonstrates stronger generalization and reasoning capabilities when identifying previously unseen addresses, offering solid technical support for improving blockchain security systems.
Scalable Blockchain Fraud Detection Using Spatial-Temporal Graph Neural Networks
The increasing adoption of blockchain technology has led to a surge in financial fraud, including money laundering, Ponzi schemes, and illicit fund transfers. Traditional fraud detection techniques, such as rule-based systems and supervised machine learning models, struggle to handle the high-volume, high-velocity, and dynamically evolving nature of blockchain transactions. These limitations necessitate a scalable and adaptive approach to detect fraudulent activities efficiently. This study introduces a Spatial-Temporal Graph Neural Network (STGNN)-based fraud detection framework, specifically designed for scalable anomaly detection in large-scale blockchain networks. By modeling blockchain transactions as a spatial-temporal graph, the proposed system captures structural dependencies between wallets and temporal patterns of fund movements. The STGNN model employs graph convolutional networks (GCN) or graph attention networks (GAT) for spatial feature extraction and gated recurrent units (GRU) or temporal convolutional networks (TCN) for sequential fraud pattern recognition. Additionally, to ensure scalability, the framework incorporates graph partitioning techniques, parallelized mini-batch training, and distributed processing, enabling real-time fraud detection across high-throughput blockchain networks. Extensive experiments conducted on Bitcoin and Ethereum transaction datasets demonstrate that the STGNN model achieves higher accuracy, lower false positive rates, and improved computational efficiency compared to rule-based fraud detection systems, supervised ML models, and static GNNs. Case studies further confirm the model’s effectiveness in detecting large-scale fraud schemes, such as DeFi exploits, cross-chain laundering, and coordinated illicit transactions. This research highlights the potential of graph-based deep learning techniques in blockchain security, providing a foundation for future advancements in scalable fraud detection, cross-chain anomaly detection, and decentralized financial security monitoring.
Stochastic Processes in Applied Physics: From Brownian Motion to Financial Modeling
Stochastic processes are mathematical models used to describe systems that evolve over time with inherent randomness. This paper explores the application of stochastic processes in applied physics, focusing on their use in modeling phenomena such as Brownian motion and financial systems. The study begins with a foundational overview of stochastic processes, detailing their mathematical framework and key concepts. It then transitions to specific applications, including classical examples from statistical mechanics and modern applications in financial modeling. By examining the methodologies and results of various studies, this paper aims to highlight the versatility and significance of stochastic processes in understanding and predicting complex systems in physics and finance.
Copyright (c) 2024 Frontiers in Applied Physics and Mathematics
The Physics of Solitons: Mathematical Insights into Stable Waveforms
Solitons are stable, self-reinforcing wave packets that maintain their shape while propagating through a medium. These waveforms arise in various physical contexts, including fluid dynamics, optics, and plasma physics, and can be described by specific non-linear partial differential equations. This paper provides a comprehensive overview of the mathematical framework that governs solitons, focusing on key equations like the Korte Weg-de Vries (KdV) equation, the nonlinear Schrödinger equation, and the sine-Gordon equation. We explore the underlying principles that ensure soliton stability and discuss their applications in various fields of physics. By examining the mathematical structures and methods such as inverse scattering transform and Bäcklund transformations, we highlight the interplay between mathematics and physics in understanding soliton phenomena. This study not only elucidates the rich theory behind solitons but also emphasizes their practical implications in modern scientific research.
Mathematical Techniques for Analyzing Nonlinear Optical Phenomena
Nonlinear optical phenomena occur when the response of a material to light is non-proportional to the electric field of the light wave, resulting in diverse and complex behaviors such as harmonic generation, self-phase modulation, and soliton formation. These phenomena have become pivotal in advancing fields such as telecommunications, laser technology, and quantum optics. This article explores the mathematical techniques used to analyze and understand nonlinear optical processes, offering a comprehensive overview of theoretical approaches including perturbation theory, coupled-mode theory, and the nonlinear Schrödinger equation (NLSE). The role of these mathematical tools in modeling second- and third-order nonlinearities is examined, alongside applications in optical fiber systems and photonic crystal technologies. Advanced computational methods, including numerical solvers and finite-difference time-domain (FDTD) simulations, are also discussed as essential tools for solving complex, nonlinear optical problems.
Tensor Calculus in General Relativity: A Bridge Between Mathematics and Theoretical Physics
Tensor calculus serves as the mathematical framework underpinning the theory of General Relativity, bridging the gap between abstract mathematical concepts and their physical applications. This paper explores the fundamental principles of tensor calculus, its critical role in formulating Einstein's field equations, and its impact on our understanding of spacetime and gravity. We delve into key tensor operations, including differentiation and contraction, and examine their applications in solving problems in general relativity. By highlighting both historical developments and modern advancements, this study underscores the profound connection between tensor calculus and theoretical physics, demonstrating its significance in describing the universe's structure and dynamics.
Mathematical Frameworks for Analyzing Quantum Field Theory
Quantum Field Theory (QFT) provides a foundational framework for understanding particle physics and interactions at a fundamental level. This article presents a comprehensive overview of mathematical frameworks utilized in the analysis and formulation of QFT. Emphasis is placed on the integration of differential geometry, algebraic structures, and functional analysis within the context of QFT. The paper discusses the role of various mathematical tools such as Lie groups, tensor calculus, and gauge theory in the development and application of QFT models. Through detailed examples and theoretical exposition, this work aims to bridge the gap between abstract mathematical concepts and their practical implementation in theoretical physics.
The Mathematics of Wave Propagation: Applications in Acoustics and Electromagnetism
Wave propagation plays a fundamental role in understanding various physical phenomena, particularly in the fields of acoustics and electromagnetism. This paper explores the mathematics underpinning wave propagation, focusing on the solutions to wave equations, boundary conditions, and the effects of medium properties on wave behavior. Applications in acoustics include sound wave propagation in different media, while electromagnetism examines the behavior of electromagnetic waves in free space and through materials. This study demonstrates the interconnection between mathematics and physics, offering insights into practical applications such as signal transmission, noise reduction, and optical technologies.
Chaos Theory in Applied Physics: Mathematical Insights into Unpredictable Systems
Chaos theory, a branch of mathematics focusing on systems that exhibit sensitive dependence on initial conditions, has profound implications for applied physics. This article explores the fundamental principles of chaos theory, its mathematical frameworks, and its applications in understanding unpredictable physical systems. By examining nonlinear dynamics, bifurcations, and attractors, we provide insights into how chaotic behavior emerges and influences various physical phenomena. The discussion extends to real-world applications, such as weather forecasting, fluid dynamics, and quantum mechanics, illustrating how chaos theory contributes to a deeper understanding of complex systems.
Fractal Geometry in Nature: Applications in Physics and Mathematical Theory
Fractal geometry, a field that explores the complex patterns and structures found in nature, has profound implications for both physics and mathematical theory. This article investigates the application of fractal geometry in various natural phenomena and its role in advancing physical theories. By analyzing fractal patterns in geological formations, biological systems, and cosmic structures, this study highlights how fractal geometry provides a deeper understanding of the inherent complexity and scaling laws in nature. Additionally, the article examines the integration of fractal principles into mathematical models and physical theories, illustrating their impact on fields such as chaos theory, quantum mechanics, and cosmology. Through a comprehensive review of existing literature and recent developments, this paper underscores the significance of fractal geometry in bridging theoretical concepts and empirical observations.
Topological Insulators: A Mathematical Perspective on Quantum Phenomena
Topological insulators represent a class of materials with unique electronic properties arising from their topological order rather than their symmetry. These materials exhibit insulating behavior in their bulk but support robust conducting states on their surfaces or edges. This paper presents a mathematical perspective on topological insulators, exploring the theoretical frameworks that underpin their behavior. We delve into the mathematical foundations of topological invariants, band theory, and the connection between geometry and quantum states. By examining these concepts through a rigorous mathematical lens, we aim to provide deeper insights into the mechanisms driving the fascinating phenomena observed in topological insulators.
Optimization Techniques in Applied Mathematics: From Physics Simulations to Real-World Problems
Optimization techniques play a critical role in applied mathematics, enabling the effective and efficient solving of complex problems across various fields. This paper explores the application of optimization methods from theoretical frameworks to practical scenarios, particularly focusing on their utilization in physics simulations and real-world problems. We review classical optimization techniques, including linear programming and nonlinear optimization, as well as advanced methods such as evolutionary algorithms and machine learning-based approaches. By examining case studies and current applications, we highlight the effectiveness of these techniques in addressing real-world challenges, from engineering design to financial modeling. The insights provided offer a comprehensive understanding of how optimization contributes to advancing both theoretical research and practical problem-solving.
Numerical Methods for Solving Partial Differential Equations in Applied Physics
Partial Differential Equations (PDEs) are fundamental in modeling various physical phenomena in applied physics, including heat conduction, fluid dynamics, and electromagnetic fields. Numerical methods have become essential tools for solving these PDEs due to their complexity and the limitations of analytical solutions. This paper provides a comprehensive overview of numerical methods employed in solving PDEs, focusing on finite difference methods, finite element methods, and spectral methods. We discuss the theoretical foundations, implementation strategies, and practical applications of these methods. Special attention is given to the accuracy, stability, and efficiency of different numerical approaches, along with recent advancements and emerging techniques in the field. Through illustrative examples and case studies, this paper aims to highlight the importance of numerical methods in advancing the understanding and technological applications of PDEs in physics.
The Role of Symmetry in Physics: Group Theory Applications in Modern Physics
Symmetry is a fundamental concept in physics that has significantly shaped our understanding of the natural world. This paper explores the role of symmetry in modern physics through the lens of group theory, highlighting its applications across various domains including particle physics, condensed matter physics, and cosmology. By examining the principles of group theory and their application to physical systems, we elucidate how symmetry considerations lead to profound insights and predictions. We discuss the impact of symmetry on conservation laws, particle classification, and the unification of fundamental forces, illustrating the integral role symmetry plays in advancing theoretical and experimental physics.
Nonlinear Dynamics in Complex Systems: A Mathematical Approach
Nonlinear dynamics are pivotal in understanding complex systems, where traditional linear models fail to capture the intricacies of real-world phenomena. This paper explores the mathematical foundations of nonlinear dynamics and their applications across various complex systems. We begin with an introduction to the core concepts of nonlinear dynamics, including chaos theory, bifurcation theory, and fractals. We then discuss the mathematical tools used to analyze nonlinear systems, such as differential equations and attractor reconstruction. The paper further investigates practical applications in fields such as engineering, biology, and economics. By emphasizing both theoretical and applied aspects, this work aims to provide a comprehensive overview of how nonlinear dynamics can be harnessed to model and predict the behavior of complex systems.
Quantum Computing: The Intersection of Physics and Advanced Mathematics
Quantum computing represents a revolutionary convergence of physics and advanced mathematics, promising to transform computational paradigms by leveraging quantum mechanical principles. Unlike classical computers, which process information in binary bits, quantum computers utilize quantum bits or qubits that can exist in multiple states simultaneously due to superposition and entanglement. This intersection of quantum physics and advanced mathematical algorithms has the potential to solve complex problems beyond the reach of classical computing, including cryptography, optimization, and drug discovery. This paper explores the foundational principles of quantum computing, the mathematical frameworks that underpin its operations, and the implications for future technological advancements. Through a detailed examination of both theoretical and practical aspects, this study aims to provide a comprehensive overview of how quantum computing bridges the domains of physics and mathematics.
Mathematical Modeling of Fluid Dynamics: Applications in Engineering and Environmental Science
Mathematical modeling of fluid dynamics plays a pivotal role in various domains, particularly in engineering and environmental science. This article provides an in-depth exploration of mathematical techniques used to model fluid flow, emphasizing their applications in solving complex engineering problems and addressing environmental challenges. By analyzing the governing equations of fluid dynamics, such as the Navier-Stokes equations, and applying numerical methods for their solution, the study highlights how these models contribute to advancements in engineering design, environmental monitoring, and resource management. The paper also discusses the integration of these models with empirical data and the development of innovative simulation tools that enhance predictive capabilities. Through case studies and practical examples, this article illustrates the transformative impact of fluid dynamics modeling on contemporary scientific and engineering practices.