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.