Multidisciplinary Engineering Science Open

This review aims to synthesize and critically analyze the state-of-the-art methodologies in uncertainty quantification (UQ) for high-dimensional engineering systems, focusing on Polynomial Chaos Expansion, Bayesian inference, and active learning frameworks as core paradigms for scalable and interpretable uncertainty management. This qualitative review employed a systematic literature analysis approach. A total of twelve peer-reviewed journal articles published between 2010 and 2024 were purposefully selected from leading engineering and computational science databases, including IEEE Xplore, ScienceDirect, SpringerLink, and Wiley Online Library. The inclusion criteria emphasized methodological rigor, relevance to high-dimensional UQ, and the presence of at least one of the three focal paradigms. Data collection relied exclusively on a literature-based review process, followed by qualitative thematic analysis using NVivo 14 software. The coding process involved open, axial, and selective coding to identify emerging themes, ensuring theoretical saturation. The resulting conceptual framework categorized the extracted data into three major themes—spectral methods (Polynomial Chaos), probabilistic inference (Bayesian approaches), and adaptive learning (active sampling)—and their interconnections. The analysis revealed a convergent methodological evolution in UQ research. Polynomial Chaos methods demonstrated robust efficiency in surrogate modeling and spectral uncertainty propagation through sparse and adaptive expansions. Bayesian inference emerged as a statistically coherent framework for parameter calibration, model selection, and posterior uncertainty representation, supported by scalable techniques such as Hamiltonian Monte Carlo and variational inference. Active learning proved essential for adaptive data acquisition and surrogate refinement, significantly reducing computational costs through informed sampling. Collectively, the three paradigms exhibited strong complementarity, forming hybrid UQ architectures that combine interpretability, scalability, and computational sustainability. Modern high-dimensional UQ research increasingly integrates spectral, Bayesian, and adaptive learning paradigms into unified frameworks capable of handling nonlinear, data-scarce, and computationally intensive problems. This triadic convergence represents a methodological shift toward interpretable, data-efficient, and scalable uncertainty quantification suitable for next-generation engineering simulations.

This review aims to synthesize and critically evaluate recent advancements in coupling computational fluid dynamics (CFD) and computational structural mechanics (CSM) at exascale levels, focusing on solver paradigms, load balancing, algorithmic scalability, and data management challenges in massively parallel environments. A qualitative systematic review design was employed to consolidate insights from cutting-edge studies on exascale multiphysics coupling. Sixteen peer-reviewed articles published between 2018 and 2025 were selected from Scopus, Web of Science, IEEE Xplore, and ScienceDirect, using keywords such as “exascale CFD,” “CSM coupling,” “monolithic solver,” “partitioned framework,” “load balancing,” and “parallel I/O.” Data collection was conducted exclusively through literature analysis, and coding was performed using Nvivo 14 software. Thematic analysis followed open, axial, and selective coding to extract conceptual relationships among solver architectures, scalability bottlenecks, and I/O strategies. Analytical saturation was reached after the sixteenth study, ensuring comprehensive thematic convergence across the dataset. Five dominant themes emerged: (1) solver coupling paradigms, (2) load balancing and parallel scalability, (3) I/O and data management, (4) algorithmic and numerical scalability, and (5) emerging trends and future directions. Results indicate that partitioned solvers provide modularity and flexibility but struggle with communication overhead at large node counts, while monolithic frameworks achieve greater numerical robustness at higher computational costs. Dynamic load balancing and hybrid MPI + OpenMP or GPU parallelism were identified as key enablers of exascale scalability. Efficient I/O frameworks such as ADIOS2 and HDF5, along with in-situ data processing and hierarchical storage, were critical for maintaining performance sustainability. The integration of machine learning, fault tolerance, and hybrid coupling strategies defines the next frontier of CFD/CSM research. Exascale CFD/CSM coupling requires co-designed strategies that integrate solver stability, load adaptivity, and efficient data movement. The review underscores that achieving exascale readiness is less a matter of hardware scale and more a function of algorithmic intelligence, communication efficiency, and workflow resilience.

This review aims to synthesize recent advances in scientific machine learning (SciML) for solving partial differential equations (PDEs), focusing on operator learning, surrogate modeling, and error-controlled multi-fidelity frameworks that integrate data-driven intelligence with physical consistency. This study adopted a qualitative, interpretive review design based on a systematic literature analysis of thirteen peer-reviewed journal articles published between 2019 and 2025. The data collection process relied exclusively on scholarly databases such as Scopus, ScienceDirect, and IEEE Xplore, targeting works addressing neural operator architectures, hybrid physics–ML couplings, and multi-fidelity adaptation. All sources were imported into Nvivo 14 software for coding and thematic synthesis. Open, axial, and selective coding cycles were performed until theoretical saturation was achieved. Four main categories—operator learning paradigms, surrogate and reduced-order models, error-controlled multi-fidelity schemes, and computational integration—were extracted and structured according to their conceptual relationships and methodological contributions. The review identified that operator learning (e.g., DeepONet, Fourier Neural Operator, and physics-informed variants) provides a scalable framework for learning function-to-function mappings across PDE families. Surrogate modeling emerged as an efficient approach for reduced-order representation and hybrid PDE–ML coupling, while sparse, compressive, and latent-space techniques improved model interpretability and efficiency. Multi-fidelity architectures, integrating uncertainty quantification and adaptive refinement, offered robust mechanisms for cost-accuracy optimization and error control. Finally, the implementation trend emphasized high-performance computing, benchmarking (PDEBench), hybrid symbolic–numeric integration, and reproducibility practices as essential to operational deployment. Scientific machine learning for PDEs is transitioning from experimental novelty to a mature computational paradigm that unifies physics-informed theory, data-driven surrogacy, and adaptive error control. Its promise lies in producing generalizable, trustworthy, and computationally efficient solvers that can accelerate discovery across domains such as fluid mechanics, climate modeling, and structural dynamics while maintaining physical interpretability and numerical rigor.

This review aims to synthesize current interdisciplinary research on the integration of Urban Air Mobility (UAM) systems, focusing on how airspace management, noise footprints, and ground infrastructure interact to shape safe, efficient, and socially acceptable urban aerial transportation networks. This qualitative systematic review analyzed 18 peer-reviewed articles published between 2018 and 2025, selected from Scopus, IEEE Xplore, ScienceDirect, and Web of Science. Inclusion criteria required explicit coverage of at least one of the three focal UAM domains: airspace management, noise modeling, or ground infrastructure planning. Using NVivo 14, all sources were thematically coded through an inductive three-stage process—open, axial, and selective coding—until theoretical saturation was achieved. The analysis extracted recurring conceptual relationships and operational frameworks that underpin the practical and regulatory challenges of UAM integration. Three main themes and 18 subthemes were identified. The first theme, Airspace Management and Operational Integration, revealed priorities such as dynamic traffic management, regulatory harmonization, and CNS technological enablers for safety and data security. The second theme, Noise Footprints and Environmental Compatibility, emphasized advances in acoustic modeling, psychoacoustic perception studies, and community-centered noise mitigation policies. The third theme, Ground Infrastructure and Urban Integration, covered vertiport design, multimodal connectivity, energy systems, digital twins, and sustainable business models. The synthesis demonstrated strong interdependencies among these domains—particularly the coupling of noise constraints with airspace capacity and vertiport siting decisions—highlighting the necessity for cross-domain co-design frameworks. Urban air mobility integration demands an interdisciplinary, system-of-systems approach that harmonizes regulatory frameworks, technological innovation, environmental stewardship, and social acceptance. The findings call for coordinated policy, adaptive airspace governance, and community engagement to ensure sustainable deployment of UAM ecosystems in future smart cities.

This review aims to synthesize and conceptualize the convergence of predictive maintenance, digital twin ecosystems, and energy-aware operations in the transformation of smart railway systems toward reliability, adaptability, and sustainability. This study employed a qualitative systematic review design, focusing exclusively on secondary data derived from peer-reviewed literature. A total of 13 articles published between 2015 and 2025 were selected from major databases, including Scopus, IEEE Xplore, and ScienceDirect, using targeted keywords related to smart railways, predictive maintenance, digital twins, and energy optimization. Data collection followed a rigorous screening process based on relevance, methodological quality, and conceptual contribution, continuing until theoretical saturation was achieved. The data were analyzed through thematic coding using Nvivo 14 software, applying open, axial, and selective coding to identify emergent patterns and interrelationships across the studies. The resulting framework captured the interdependencies between technological enablers, operational intelligence, and sustainable outcomes in smart railway systems. Three major themes emerged from the analysis: (1) Predictive maintenance and asset intelligence, emphasizing IoT-enabled sensing, machine learning diagnostics, and human–AI collaboration for condition-based maintenance optimization; (2) Digital twin ecosystems and cyber-physical synchronization, highlighting lifecycle integration, real-time simulation, and security governance as key enablers of system adaptability; and (3) Energy-aware operations and sustainable rail systems, demonstrating the role of algorithmic energy optimization, smart grids, and renewable integration in reducing carbon intensity and enhancing energy efficiency. The findings revealed that the interaction between these domains forms a cohesive digital ecosystem enabling real-time, self-learning, and resource-efficient railway operations. The integration of predictive maintenance, digital twin technology, and energy-aware strategies establishes a transformative model for future railways—one that balances operational reliability, environmental sustainability, and intelligent automation. However, challenges remain in standardization, data governance, and human–AI trust, necessitating further interdisciplinary research and policy development.

This review aims to synthesize and interpret existing research on how sensing, Vehicle-to-Everything (V2X) communication, and human–automation interaction jointly influence traffic flow stability in environments where connected and automated vehicles (CAVs) coexist with human-driven vehicles (HDVs). A qualitative review methodology was employed, focusing exclusively on peer-reviewed literature published between 2015 and 2025. Searches across Scopus, Web of Science, IEEE Xplore, and ScienceDirect identified studies addressing sensing integration, cooperative perception, V2X-enabled control, and stability modeling in mixed traffic. Following relevance screening and quality appraisal, 15 key articles were selected until theoretical saturation was achieved. Data were analyzed thematically using Nvivo 14 software through open, axial, and selective coding, resulting in the identification of five overarching themes: sensing and perception, V2X communication, traffic flow stability, human–vehicle interaction, and system-level integration. Results indicated that multi-sensor fusion and cooperative perception significantly improve situational awareness but remain sensitive to environmental uncertainty and cost constraints. V2X communication—particularly 5G-V2X and edge-based architectures—emerged as essential for synchronization and safety but is hindered by latency, security, and interoperability issues. Traffic flow modeling studies revealed that CAVs enhance string stability and throughput when their penetration rate exceeds a critical threshold, although unpredictable human behaviors can reintroduce oscillations. The analysis further highlighted that human trust calibration and communication transparency strongly affect cooperation and control transitions. Finally, institutional readiness, regulatory coherence, and public education were identified as indispensable for large-scale, stable CAV deployment. CAV integration in mixed traffic requires a multidimensional approach that combines perceptual resilience, secure low-latency communication, adaptive control algorithms, and human-centered policy frameworks. Traffic stability is achieved not through isolated technological advances but through systemic coordination across technical, behavioral, and governance domains.

This study aims to synthesize and analyze current advancements in secure Internet of Things (IoT) architectures for critical infrastructure, emphasizing protocol assurance, trusted execution environments (TEEs), and post-quantum cryptographic readiness. A qualitative review design was employed to systematically examine the literature on IoT security frameworks within critical infrastructure domains. Nineteen peer-reviewed articles published between 2015 and 2025 were selected through comprehensive searches across IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, and Scopus. Inclusion criteria targeted studies addressing secure communication protocols, hardware-based trust mechanisms, and quantum-resistant encryption strategies. Data collection was limited to document analysis, and data interpretation followed a qualitative content analysis using NVivo 14. Open coding, axial categorization, and selective thematic integration were applied until theoretical saturation was achieved, producing four emergent themes that encapsulate the security, interoperability, and resilience dimensions of secure IoT stacks. The analysis revealed four major thematic dimensions: (1) protocol assurance and interoperability, focusing on secure communication frameworks and cross-layer encryption; (2) trusted execution environments and hardware roots of trust, emphasizing TEEs, secure boot mechanisms, and runtime attestation; (3) post-quantum cryptography and algorithm transition, addressing migration to quantum-safe encryption and hybrid cryptographic architectures; and (4) resilience and assurance in critical infrastructure IoT, highlighting risk management, compliance, and forensic readiness. Collectively, these dimensions illustrate a systemic evolution from isolated security mechanisms toward integrated assurance ecosystems combining hardware, software, and governance layers. Secure IoT stack design for critical infrastructures demands convergence between protocol standardization, hardware-based trust, and post-quantum preparedness. Future IoT security models should prioritize interoperability, algorithmic agility, and continuous certification to ensure operational resilience against both current and emerging cyber-physical threats.

This review investigates how RISC-V, an open and extensible instruction set architecture, can be effectively adopted in safety-critical embedded systems through integrated hardware/software co-design strategies and structured assurance cases that ensure compliance with functional safety standards. A qualitative systematic review design was employed to synthesize the state of research on RISC-V implementation within safety-critical environments. Seventeen peer-reviewed journal articles and conference papers published between 2015 and 2025 were selected from IEEE Xplore, Scopus, Web of Science, and ACM Digital Library databases based on inclusion criteria emphasizing RISC-V architectures, safety assurance, and verification frameworks. Data collection consisted exclusively of literature review. Thematic analysis using NVivo 14 software was conducted through open, axial, and selective coding, with theoretical saturation achieved at the seventeenth article. Four core themes were extracted: (1) hardware/software co-design paradigms, (2) safety assurance and certification frameworks, (3) open-source ecosystem and verification governance, and (4) energy–latency trade-offs and performance assurance. Results reveal that modular co-design approaches in RISC-V enable domain-specific optimizations while maintaining deterministic timing and verifiability. Structured assurance cases—built on Goal Structuring Notation (GSN) and model-based verification—are emerging as credible mechanisms for aligning open-hardware transparency with certification expectations such as ISO 26262, DO-254, and IEC 61508. The open-source RISC-V ecosystem enhances reproducibility, community validation, and toolchain verification but introduces challenges in provenance tracking and standardization. Furthermore, energy-aware design techniques like dynamic voltage and frequency scaling (DVFS) can improve efficiency without compromising real-time safety guarantees when combined with rigorous timing validation. Collectively, the findings highlight a co-evolution of technical innovation and assurance methodology that redefines safety in open architectures. RISC-V’s adoption in safety-critical domains depends on unifying co-design practices with auditable assurance frameworks that demonstrate both functional safety and transparency. Future progress will hinge on standardized open-hardware certification models, formal verification integration, and collaborative governance to balance innovation with accountability.