Organizers: Dr. Gerasimos Rigatos, Unit of Industrial Automation, Industrial Systems Institute, 26504, Rion Patras, Greece, email: grigat@ieee.org.

Dr. Gennaro Cuccurullo, Department of Industrial Engineering, University of Salerno, 84084, Fisciano, Italy, email: gcuccurullo@unisa.it.

Dr. Masoud Abbaszadeh, Department of Electrical Computer and Systems Engineering, Rensselaer Polytechnic Institute, Troy, New York, 12065, USA email: masouda@ualberta.ca.tt

Dr. Pierluigi Siano, Department of Management and Innovation Systems, University of Salerno, 84084, Fisciano, Italy, email: psiano@unisa.it.

Dr. Taniya Ghosh, IGIDR, Institute of Development Research, 400065, Mumbai, India
email: taniya.ghosh@gmail.com.

The optimized functioning of complex nonlinear dynamical systems, as for instance robotic, electromotion and electric power systems is based on the ability to solve the associated nonlinear control and estimation problems. Main approaches for the solution of nonlinear control problems are: (i) control with global
linearization methods, (ii) control with approximate linearization methods, (iii) control with Lyapunov stability theory techniques (machine learning-based adaptive control when the dynamic model of the con- trolled is unknown). Main approaches for the solution of nonlinear state estimation problems are: (i)
filtering and observers with global linearization techniques, (ii) filtering and observers with approximate linearization techniques, (iii) state estimation with observers that rely on Lyapunov stability theory. Main objectives in the control of complex nonlinear dynamical systems are to formulate: (i) stability conditions
for the nonlinear control methods, (ii) stability conditions for the nonlinear state estimation methods, (iii) stability conditions for the joint control and state estimation schemes.
The proposed Special Session on Advanced nonlinear control and estimation for complex dynamical systems aims at addressing all aforementioned issues. Topics of interest include but are not limited to:
(a) Nonlinear optimal control, flatness-based control with transformation to canonical forms, multi-loop flatness-based control, Lie algebra-based control, nonlinear model predictive control, sliding-mode control, backstepping control, multi-model optimal feedback control and adaptive control,
(b) Nonlinear Kalman Filtering, Extended and Unscented Kalman Filtering, Particle Filtering, H-infinity Kalman Filtering, dis- tributed nonlinear filtering, sliding-mode observers, and diffeomorphisms-based nonlinear observers.
(c) Applications to robotics, mechatronics, electromotion, electric vehicles, electric power systems, renewable energy systems, industrial processes, aerospace systems, biomedical systems and financial systems.

Prospective authors are requested to submit their articles, until the 30th of June 2026 to:

1. Dr. Gerasimos Rigatos, Unit of Industrial Automation, Industrial Systems Institute, 26504, Rion Patras, Greece, email: grigat@ieee.org.
2. Dr. Gennaro Cuccurullo, Department of Industrial Engineering, University of Salerno, 84084, Fisciano, Italy, email: gcuccurullo@unisa.it.
3. Dr. Masoud Abbaszadeh, Department of Electrical Computer and Systems Engineering, Rensselaer Polytechnic Institute, Troy, New York, 12065, USA email: masouda@ualberta.ca.tt
4. Dr. Pierluigi Siano, Department of Management and Innovation Systems, University of Salerno, 84084, Fisciano, Italy, email: psiano@unisa.it.
5. Dr. Taniya Ghosh, IGIDR, Institute of Development Research, 400065, Mumbai, India
   email: taniya.ghosh@gmail.com.

Organizers: Prof. Dr. Mohammad Mohammadi Aghdam, Mechanical Engineering Department, Amirkabir University of Technology, Tehran , Iran
Dr. Ali Fallah, Assistant Professor, Department of Automotive Engineering, Atılım University, Ankara, Türkiye

Physics-informed machine learning (PIML) has emerged as a powerful paradigm for solving and analyzing partial differential equations governing complex engineering systems by integrating physical laws directly into learning architectures. Among these methods, Physics-Informed Neural Networks (PINNs) and related operator-learning frameworks provide mesh-free, data-efficient alternatives to classical numerical techniques, while preserving physical consistency and interpretability.
This symposium aims to bring together researchers working at the intersection of numerical analysis, applied mathematics, and computational mechanics to present recent advances in physics-informed machine learning methods. Particular emphasis will be placed on the numerical analysis aspects of these approaches, including formulation, stability, convergence behavior, error assessment, and benchmarking against established numerical methods such as finite element, finite difference, and spectral techniques.
Contributions addressing applications in solid and structural mechanics, heat and mass transfer, fluid mechanics, wave propagation, additive manufacturing, and multiphysics or nonlinear systems are especially welcome. The symposium seeks to foster dialogue between the numerical analysis and engineering communities, promoting the development of reliable, physics-consistent, and scalable machine-learning-based solvers for real-world mechanical and multiphysics problems.

Organizer: Prof Ben Evans, Swansea University, UK, Swansea University Bay Campus, Fabian Way, Swansea, Wales, UK SA1 8EN

Email: b.j.evans@swansea.ac.uk

Description:
The scope of this session covers the broad spectrum of research spanning theoretical and applied computational optimisation approaches. We are interested in contributions on approaches for optimisation used across science and engineering in fields ranging from aerospace engineering to pure mathematics. We encourage contributions from both established and early-career researchers in the following topic areas:

• Mathematical and theoretical optimisation
• Algorithms for optimisation
• Metaheuristics and nature-inspired algorithms
• Combinatorial Optimisation
• Optimisation in Machine Learning
• Optimisation with Uncertainty
• Domain Specific Applications
• Novel design parameterisation approaches
• Mesh-based methods in Optimisation

Organizers:

• Mangiameli Michele, Department of Civil Engineering and Architecture (DICAR), University of Catania, Viale Andrea Doria, 6, 95125 Catania (Italy)
• Mussumeci Giuseppe, Department of Engineering, University of Messina, Via Salita Sperone, c.da Papardo Cap: 98166, Messina (Italy)
• Muscato Giovanni, Department of Electrical, Electronics and Computer Engineering (DIEEI), University of Catania, Viale Andrea Doria, 6, 95125 Catania (Italy)
• Guastella Dario, Department of Electrical, Electronics and Computer Engineering (DIEEI), University of Catania, Viale Andrea Doria, 6, 95125 Catania (Italy)
• Ragusa Maria Alessandra, Dipartimento di Matematica e Informatica, University of Catania,Viale Andrea Doria, 6, 95125 Catania, (Italy)
• Cappello Annalisa, Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo, Catania, Italy
• Greco Filippo, Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo, Catania, Italy
• Sutera Giuseppe, Department of Electrical, Electronics and Computer Engineering (DIEEI), University of Catania, Viale Andrea Doria, 6, 95125 Catania (Italy)
• Pappalardo Giuseppina, Department of Civil Engineering and Architecture (DICAR), University of Catania, Viale Andrea Doria, 6, 95125 Catania (Italy)

email: michele.mangiameli@unict.it, giuseppe.mussumeci@unime.it, gmuscato@dieei.unict.it, dario.guastella@dieei.unict.it, maragusa@dmi.unict.it, annalisa.cappello@ingv.it, giuseppe.sutera@unict.it, filippo.greco@ingv.it, giuseppina.pappalardo1@unict.it

This Symposium is dedicated to the geomatics approaches used for surveying, monitoring, and mathematical modeling of the territory, both with classical techniques and innovative technologies, related also to natural and anthropic risk assessment. Geomatics approaches involve the use of the latest generation of laser scanners, UAVs (Unmanned Aerial Vehicles) or UGVs (Unmanned Ground Vehicles) and satellite technologies, for an enhanced monitoring of natural and built environment. Particular attention will be given to the use of free and open- source information technologies (GIS software and application, DBMS, photogrammetry, Remote sensing, etc.), digital twin for the management of data acquired in the field and artificial intelligence algorithms. Results achieved with the help of photo-realistic simulation environments are welcomed as well. This session will allow attendees to review and share knowledge and experience about environmental monitoring and mathematical modelling, reinforcing the cooperation between applied mathematics, engineering, environmental and
Earth sciences and remote sensing.

Organizer: Xingyu Liu, Independent Researcher, former postdocrotal researcher,
Hebei province, Qinhuangdao City, Haigang District, Xigang zhen, Hanhaifengjing, 12-1-102

• Email:  xingyu.liu1025@outlook.com

This session focuses on the interplay between dynamical systems, statistical modeling, and modern mathematical applications. Topics include stochastic dynamics, time series analysis, statistical inference for dynamical systems, computational statistics, and cross-disciplinary methods in applied mathematics. The session aims to bring together researchers to present high-quality contributions, share recent advances, discuss open problems, and promote academic cooperation.

Organizers: Prof. Guo-Cheng Wu, School of Mathematics and Statistics, Chongqing University of Posts and Telecommunications, Chongqing, 400065, PR China

Dr. Babak Shiri, Key Laboratory of Numerical Simulation of Sichuan Provincial Universities，School of Mathematics and Big Data,
Neijiang 641100, PR China

Dr. Cheng Luo, School of Mathematics and Statistics, Southwest University, 400715, Chongqing, PR China

Emails: wuguocheng@gmail.com, shiri@tabrizu.ac.ir, mathluo@yeah.net

This symposium is devoted to new results in fractional calculus. It welcomes students and researchers to share new ideas and have further cooperation on, but not limited to the following topics:

• Fractional dynamical systems and machine learning;
• Discrete fractional calculus;
• Uncertain Fractional calculus.

Organizer: Suman Maity, Department of Mathematics, Raja Narendralal Khan Women’s College (Autonomous), Gopa Palace, Midnapore-721102, West Bengal, India

Email: maitysuman2012@gmail.com

As real-world systems grow in complexity, the ability to model uncertainty and vagueness becomes critical. This symposium serves as a focused forum for researchers and industry practitioners to explore the evolution of fuzzy logic from its foundational roots to modern hybrid frameworks.
The session will delve into Type-2 Fuzzy Sets, Intuitionistic Fuzzy Logic, Dense fuzzy set, Cloudy fuzzy set, Hesitant fuzzy set, Picture fuzzy set, Monsoon Fuzzy set, q-rung orthopair fuzzy set, etc., and the integration of fuzzy systems with Machine Learning and Neural Networks. The primary objective is to highlight how these “modern” fuzzy tools provide robust solutions for decision-making in high-stakes environments such as autonomous systems, supply chain logistics, and environmental modelling. Key discussion points will include:

• Numerical Methods: Improving the efficiency of fuzzy inference systems.
• Hybrid AI: Combining the interpretability of fuzzy logic with the predictive power of deep learning.
• Optimization: Utilizing fuzzy constraints in complex inventory and operations research problems.

Organizer: Emanuel Guariglia, Kean University, USA

email: emanuel.guariglia@gmail.com

In this session, we invite and welcome review, expository, and original results dealing with recent advances in wavelet analysis, fractal geometry and and fractional PDEs. From a more general point of view, we encourage contributions on all theoretical and practical results in mathematics, physics, and engineering focused on this topic.

The main topics of this Special Issue include (but are not limited to):
1 Fractality of dynamical systems.
2 Wavelet analysis and fractional PDEs.
3 Fractional PDEs, fractal functions and applications.
4 Iterated function systems and random fractals.
5 Fractional boundary value problems and their role in applied science.
6 Wavelet theory and image processing.
7 Chaoticity, dynamical systems theory and fractional calculus.
8 Wavelet methods for integral equations.
9 Wavelet methods for fractional PDEs.
10 Brownian motion and potential theory.

Organizer: Dr Zeeshan Ali (Lecturer in Applied Mathematics), School of Science, Monash University Malaysia
Email: zeeshan.ali@monash.edu; zeeshanmaths1@gmail.com

This session focuses on recent advances in computational epidemiology, including mathematical modeling, numerical simulation, and analysis of infectious disease dynamics. Topics include dynamical systems, deterministic and stochastic models, parameter estimation, and data-driven approaches. Contributions addressing both theoretical developments and real-world applications are welcome.

Organizer: Professor Prem Kumar Singh, Department of Computer Science and Engineering, Gandhi Institute of Technology and Management-Visakhapatnam, Andhra Pradesh 530045, India

Email: premsingh.csjm@gmail.com

This symposium will focus on foundations of four valued logics and its applications. There are several methods to explore the data in four valued space. There are several examples exists in literature like Aristotle philia concepts, Nagarjuna Catuskoti logic, Syadvada and Belnap logic. Sometimes the existence of four valued attributes is based on human consciousness to represent the data in a given space. Due to which the Turiyam set and its applications are introduced. In this symposium real-world applications of four valued logic in Quantum AI, Robotics, Gen AI, LLM as well as decision sciences will be explored. The aim is to establish the mathematics of four valued logic and its connection with the current trends in applied mathematics.

Organizers: Mohammad A. AlQudah, German Jordanian University, Amman, Jordan
Maalee Almheidat, The University of Jordan, Amman, Jordan
Ayat Almomani, Yarmouk University, Irbid, Jordan
Banan Maayah, The University of Jordan, Amman, Jordan

Please send submissions to: mohammad.qudah@gju.edu.jo and CC m.almheidat@ju.edu.jo.

This mini-symposium aims to provide a dynamic platform for researchers in statistics and applied mathematics to present and discuss recent advances in theory and applications. The session will emphasize the integration of statistical modeling, computational techniques, and analytical methods in solving contemporary scientific and engineering problems. Participants will explore topics ranging from modern statistical inference and data analysis to optimization,
fractional calculus, and computational mathematics. The symposium encourages interdisciplinary contributions and fosters collaboration among young researchers, offering constructive feedback, networking opportunities, and exposure to emerging research directions.
By bridging theoretical developments with real-world applications, the session seeks to inspire innovation and support the professional growth of junior mathematicians and statisticians.

Symposium Tracks:

Track 1: Statistical Modeling, Data Analysis, and Intelligent Learning

This track focuses on modern statistical methodologies integrated with data-driven and intelligent learning techniques. It welcomes both theoretical and applied contributions addressing contemporary data challenges.
Topics include (but are not limited to):

• Statistical inference (frequentist and Bayesian analysis) 
• Missing data techniques and imputation methods 
• Regression models (linear, nonlinear, and generalized models) 
• Multivariate and high-dimensional data analysis 
• Machine learning and deep learning in statistical modeling 
• Statistical learning theory and predictive analytics 
• Time series analysis and forecasting 
• Experimental design and survey methodology 
• Data-driven applications in engineering, healthcare, economics, and social sciences 

Track 2: Optimization, Computational Methods, and Simulation

This track emphasizes advanced computational techniques, optimization frameworks, and simulation-based approaches for solving complex problems in applied mathematics and statistics.
Topics include:

• Numerical optimization (convex, non-convex, global optimization) 
• Metaheuristic and evolutionary algorithms 
• Scientific computing and numerical linear algebra 
• Modeling and simulation of complex systems 
• High-performance and parallel computing 
• Monte Carlo methods and stochastic simulation 
• Data-driven and AI-based optimization techniques 
• Inverse problems and computational modeling 
• Applications in engineering, artificial intelligence, and operations research 

Track 3: Fractional Calculus, Analytical Methods, and Kernel Techniques

This track highlights theoretical and applied advances in fractional calculus and analytical frameworks, with strong emphasis on computational and approximation techniques.
Topics include:

• Fractional differential and integral equations 
• Numerical solutions of fractional systems and fractional integrodifferential equations 
• Analytical and semi-analytical solution methods 
• Special functions and generalized polynomials 
• Stability and qualitative analysis of fractional systems 
• Nonlinear dynamics and complex systems 
• Applications in physics, biology, fluid dynamics, and control theory 
• Connections with geometric function theory and q-calculus 

Track 4: Stochastic Processes, Applied Probability, and Uncertainty Quantification

This track explores probabilistic modeling, stochastic systems, and uncertainty quantification in both theory and applications.
Topics include:

• Stochastic processes (Markov chains, diffusion processes, Lévy processes) 
• Time series and stochastic forecasting models 
• Financial mathematics and risk analysis 
• Reliability theory and survival analysis 
• Queueing systems and operations research 
• Probabilistic machine learning and uncertainty quantification 
• Random processes in engineering and applied sciences 
• Statistical and stochastic modeling under uncertainty 
• Applications in economics, insurance, and data science

Organizers: Andreas Weinmann, Technische Hochschule Würzburg-Schweinfurt, Ignaz-Schön-Straße 11, 97421 Schweinfurt
Jürgen Frikel, OTH Regensburg, Prüfeninger Str. 58, 93049 Regensburg

Email: andreas.weinmann@thws.de, juergen.frikel@oth-regensburg.de

The fields of imaging and computer vision are rapidly evolving and play a central role in applications such as medical imaging, nondestructive testing, and industrial inspection. With the emergence of new sensing technologies and data-driven approaches, the interplay between model-based methods and learning-based techniques is becoming increasingly important. While classical approaches remain fundamental, advances in machine and deep learning and computer vision offer new perspectives for addressing complex reconstruction and interpretation tasks.
This session aims to cover the full spectrum from modeling to applications, including topics such as deep learning for imaging, multimodal data fusion, feature extraction, and application-driven solutions.
By bringing together researchers from different communities, the session seeks to foster interdisciplinary exchange and highlight connections between theory, methodology, and real-world applications.

Organizers: Charu Gupta, Bhagwan Parshuram Institute of Technology, Delhi, India

Email: charu.wa1987@gmail.com

The proposed symposium aims to provide a rigorous and contemporary account of how computational intelligence and statistical learning jointly address complex, data-driven problems. It brings together high-quality, peer-reviewed contributions that develop mathematical models, inferential frameworks, and optimization techniques underlying modern intelligent systems. Emphasis is placed on integrating machine learning, evolutionary computation, and statistical inference with a solid theoretical foundation.

Organizer:   Assoc. Prof. Dr. Nor Azlina Ab. Aziz,   Faculty of Engineering & Technology,   Multimedia University Melaka 

Email: azlina.aziz@mmu.edu.my

  The symposium focuses on the integration of advanced mathematical modeling, artificial intelligence (AI), and soft computing techniques to address real-world challenges across various domains. These approaches enable the development of innovative, robust, adaptive, cost-effective, and sustainable solutions to today’s complex problems. The symposium provides a platform for interdisciplinary knowledge exchange among researchers, practitioners, and industry experts.
  Topics of interest include, but are not limited to:

        * Environmental Monitoring & Climate Solutions
        * Sustainable Agriculture & Food Systems
        * Energy Systems & Electrification
        * Smart Cities & Urban Sustainability
        * Circular Economy & Sustainable Manufacturing
        * Healthcare & Public Health Sustainability
        * Sustainable Finance & Policy

Organizer: Prof. Yousef Farhaoui Moulay Ismail University, Morocco

Email: y.farhaoui@fste.umi.ac.ma

This symposium aims to bring together researchers, academics, and practitioners working on recent advances in Artificial Intelligence, Numerical Analysis, Applied Mathematics, and Smart Systems. The symposium will focus on theoretical developments, computational methods, optimization techniques, machine learning applications, data-driven modeling, intelligent systems, and interdisciplinary applications in engineering, healthcare, education, smart environments, and emerging technologies.

The symposium encourages high-quality original contributions addressing both fundamental research and real-world applications. It will provide an international forum for exchanging innovative ideas, discussing current challenges, and fostering future collaborations.

Prof. Dr. Yilmaz Simsek, Akdeniz University Faculty of Science Department of Mathematics, 07058, Antalya, Turkey

E.mail: ysimsek@akdeniz.edu.tr, ysimsek63@gmail.com

· https://scholar.google.com.tr/citations?user=mKKkCFUAAAAJ&hl=tr
· https://orcid.org/0000-0002-0611-7141
· https://avesis.akdeniz.edu.tr/ysimsek/yayinlar
· https://publons.com/researcher/2307270/yilmaz-simsek/
· ResearcherID: C-1654-2016

The aim of this mini-symposium is to showcase the theory and applications of mathematical concepts, including scientific studies on developments in mathematical physics and engineering, mathematical chemistry and biology, and other applied sciences, including mathematical concepts, simulations, and numerical analysis methods, etc.

The main motivation for the mini-symposium was to bring together scientists working in the fields of Mathematics, Mathematical Physics, Mathematical Chemistry, Mathematical Biology, AI reliability, Probability and statistics, epidemiology, bioinformatics, genetics, management, engineering, economics, etc.

Scientific Committee

• Mustafa Alkan, Türkiye
• Abdelmejid Bayad, France
• Ayse Ceylan Yilmaz , Türkiye
• Mohand Ouamar Hernane, Algeria
• Neslihan Kilar, Türkiye
• Irem Kucukoglu, Türkiye
• Dmitry Kruchinin, Russia
• Veerabhadraiah Lokesha, India
• Gradimir V. Milovanović, Serbia
• Dora Pokaz, Croatia
• Mihaela Ribičić Penava, ,Croatia
• Yilmaz Simsek, Türkiye
• Fusun Yalcin , Türkiye

Organizers: Prof. Nabendra Parumasur and Prof. Pravin Singh, University of KwaZulu Natal School of Mathematics, Statistics and Computer Science, South Africa. Prof. V. K. Kukreja, SLIET, Punjab, India,

Email: parumasurn1@ukzn.ac.za (N. Parumasur); singhprook@gmail.com (P. Singh); vkkukreja@gmail.com (V.Kukreja)

Description of the minisymposium:
The mini-symposium focuses on numerical methods for solving PDEs and fractional PDEs with applications. Below we list the class of methods and problems which fall under the theme of the symposium.

Class of Methods:
Orthogonal Collocation
B-spline collocation methods
Orthogonal Collocation on Finite elements
Pseudo-spectral collocation methods
Sinc Collocation Methods
Differential Quadrature Methods
Wavelet Collocation Methods

Class of Problems:
Ordinary Differential Equations
Boundary value problems
Partial Differential equations
Equations of Mathematical Physics (KdV, Schrodinger, Kuramoto-Sivashinsky
equations, etc.)
Biological and Epidemiological Models
Solitary and Travelling Waves
Fractional Evolution Equations

Special Issue:
Selected papers can be considered for the special issue of the journal Fractal and Fractional. For details, please refer to the link:
Special Issue: Mathematical and Numerical Analysis of Fractional Evolution Equations and Applications
https://www.mdpi.com/journal/fractalfract/special_issues/6O6OBTD9SD
Special APC discounts are applicable for papers of high quality submitted before 30 June 2026

Organizer Affiliation: Prof. Wael A. Altabey (Professor), Department of Mechanical Engineering, Faculty of Engineering, Alexandria, University, Alexandria, 21544, Egypt

Email: wael.altabey@gmail.com

A balance between model complexity, accuracy, and computational cost is a central concern in numerical simulations. High-quality finite element (FE) mesh generation remains one of the most computationally expensive and bottlenecked phases in numerical analysis, particularly for complex geometries with multi-scale features. Traditional meshing techniques often require extensive manual intervention or suffer from element distortion, leading to numerical instability and reduced solver accuracy. This special session presents recent advancements in intelligent meshing representations that leverage machine learning algorithms, geometric deep learning, and adaptive neural representations to automate and optimize the meshing process. Ultimately, these advancements bridge the gap between computer-aided design (CAD) paradigms and automated high-fidelity engineering simulations, paving the way for next-generation, autonomous numerical analysis.
This special session aims to bring together researchers, mathematicians, and software developers at the intersection of computational mechanics and artificial intelligence to explore next-generation intelligent meshing representations. We welcome contributions focusing on the deployment of machine learning architectures—such as Graph Neural Networks (GNNs), geometric deep learning, and adaptive neural fields—to fundamentally redefine how geometries are discretized and evaluated for finite element analysis (FEA). By investigating intelligent mesh sizing functions, physics-informed neural network (PINN) guided adaptive refinement, and instant error prediction, this session intends to showcase techniques that eliminate the manual bottleneck, drastically optimize wall-clock computational time, and guarantee high-fidelity convergence.
Ultimately, this session serves as a collaborative forum to map out the transition from classical, rule-based meshing algorithms toward autonomous, end-to-end, high-quality simulation workflows capable of driving next-generation engineering paradigms.

Contributions on the following theme are welcome (but they need not be limited to this list):

Theme A: AI-Driven & Neural Mesh Representations

• Graph Neural Networks (GNNs) for non-Euclidean mesh topology optimization and node connectivity prediction.
• Geometric Deep Learning architectures interfacing directly with CAD Boundary Representations (B-Reps).
• Neural Sizing Functions and neural field definitions for instantaneous 2D and 3D geometric discretization.
• Deep learning models for structural, fluid, and multi-physics grid generation.

Theme B: Automated Quality Control & Error Estimation

• Machine Learning Classifiers for instantaneous detection of element distortion, skewness, and critical Jacobian metrics.
• Data-driven a posteriori error estimation to bypass expensive solver-driven iterative loops.
• Physics-Informed Neural Networks (PINNs) coupled with finite element solvers for localized, high-gradient boundary layer refinement.
• Surrogate modeling for mesh independence validation and mesh sensitivity analysis.

Theme C: Numerical Performance & Solver Convergence

• Impact of intelligent representations on the condition number of global stiffness matrices and iterative solver stability.
• Comparative benchmarks: Traditional adaptivity vs. machine-learning-driven adaptive refinement.
• Reduction of preprocessing wall-clock times in large-scale industrial structural, thermal, and fluid simulations.

Theme D: Autonomous & Advanced Design Pipelines

• Integration of intelligent meshing within automated Topology Optimization and generative inverse design frameworks.
• Agentic AI and LLM-assisted pipelines for autonomous CAD-to-Mesh-to-Solution orchestration.
• Handling extreme mesh distortions in large-deformation mechanics, crack propagation, and fluid-structure interactions (FSI).

Organizers: Alessia Campiche (University  uniparthenope of Naples), Alessandro Prota (University of Federico II of Naples), Francesco Monte (University of Sannio, Benevento)

Email: alessia.campiche@uniparthenope.it, alessandro.prota@unina.it, fmonte@unisannio.it

The proposed Symposium aims to gather new contributions concerning the behaviour, design, testing, and analysis of steel and composite steel structures, including papers focused on the assessment of the current state of the art as well as on innovative design approaches and analytical methods available in the literature. Particular attention is devoted to contributions addressing the design and assessment of steel structures subjected to seismic, fatigue, and robustness-related actions. Both experimental and numerical studies are welcome.

Organizer: Pierros Ntelis, Harbin Institute of Technology, School of Physics, China and Institute of Theoretical Physics, at National University of Uzbekinstan

Email: ntelis.pierros@gmail.com

This symposium focuses on the foundational and frontier roles of symmetry principles, tensor structures, and mathematical models in advancing modern mathematics, cosmology, and astrophysics. Contributions are invited across three interconnected themes:
(1) the application of established symmetries and tensors (including advanced manifold–metric pairs) to contemporary cosmological and astrophysical models;
(2) the development of innovative models that leverage standard and extended tensor frameworks, fractional calculus, and manifold–metric pairs toward potential unification of existing theories; and
(3) the construction of novel or modified symmetry principles and tensor architectures—such as broken symmetries, non-standard symmetries, generalized tensors, fractional calculus, or advanced manifold–metric formulations—and exploration of their mathematical, cosmological, and astrophysical implications. The symposium aims to bridge theoretical innovation with observational application, fostering interdisciplinary dialogue between mathematicians, cosmologists, and astrophysicists.