Organizer: Dr Anant Pratap Singh, Department of Mathematics and Applied Sciences, JK Lakshmipat University, Jaipur India.
Email : anant.singh@jklu.edu.in
Modern challenges in mathematical physics and engineering increasingly demand computational frameworks capable of resolving non-local dynamics, inherent stochasticity, and high-dimensional parameter spaces. This symposium provides a rigorous, interdisciplinary forum dedicated to the latest advancements at the intersection of Fractional Modelling, Stochastic Partial Differential Equations (SPDEs), and Scientific Machine Learning (SciML), with a unifying emphasis on Uncertainty Quantification (UQ).By integrating classical numerical analysis with emerging data-driven paradigms, this session aims to address the theoretical and computational bottlenecks that limit our capacity to predict complex systems. A core objective is to explore how physics-informed AI architectures and operator learning can complement traditional grid-based methods to enhance computational efficiency. The scope of this symposium is deliberately broad, spanning fundamental algorithm design to domain-specific applications. We welcome contributions that explore novel finite element methods, spectral methods, scalable parallel solvers, and hybrid AI-driven mathematical frameworks.