Submission Deadline: 30 April 2027 View: 40 Submit to Special Issue
Prof. Leonardo Di G. Sigalotti
Email: leonardo.sigalotti@gmail.com
Affiliation: Departamento de Ciencias Básicas, Universidad Autónoma Metropolitana, Azcapotzalco Campus, Ciudad de México, Mexico
Research Interests: computational fluid dynamics, numerical astrophysics, numerical analysis, error analysis and consistency of particle methods, heat and mass transfer, multiphase and multicomponent flows
Prof. Carlos A. Vargas
Email: cvargas@azc.uam.mx
Affiliation: Departamento de Ciencias Básicas, Universidad Autónoma Metropolitana (UAM), Azcapotzalco Campus, Mexico City, Mexico
Research Interests: geophysics, seismotectonics, geodynamics, basin analysis, hydrocarbon exploration
Dr. Carlos E. Alvarado-Rodríguez
Email: ce.alvarado@ugto.mx
Affiliation: Dirección de Apoyos para la Consolidación de la Comunidad Científica y Humanística, Secretaría de Ciencia, Humanidades, Tecnología e Innovación (SECIHTI), Mexico City, Mexico
Research Interests: computational fluid dynamics, smoothed particle hydrodynamics, software development, multicomponent and multiphase flows, heat and mass transfer, flow in porous media, microbial kinetics simulation
Smoothed Particle Hydrodynamics (SPH) has matured from a specialized astrophysical tool into a robust, meshless framework capable of tackling complex engineering challenges involving large deformations, free-surface flows, and multi-physics interactions. However, as the computational community moves toward the exascale era and the widespread adoption of data-driven modeling, SPH faces a critical inflection point.
The significance of this Special Issue lies in its focus on the "Next Decade" of development. Traditional SPH often struggles with high computational costs and the selection of optimal smoothing parameters. By integrating Artificial Intelligence (AI) and Machine Learning (ML), researchers are now developing Physics-Informed Neural Networks (PINNs) and surrogate models that can predict particle behavior with unprecedented speed. Simultaneously, the leap in High-Performance Computing (HPC), specifically GPU-accelerated solvers, is allowing SPH to move beyond academic benchmarks into massive, real-world industrial simulations.
This theme represents a significant technical advancement by merging the Lagrangian flexibility of SPH with the predictive power of AI and the raw throughput of modern hardware. This aligns perfectly with the scope of CMES-Computer Modeling in Engineering & Sciences, which prioritizes innovative computational methods and their application across engineering and the sciences. This issue aims to serve as a high-impact venue for the latest breakthroughs in hybrid AI-SPH models, advanced GPU architectures, and large-scale multi-physics coupling.
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