Advanced Computational Methods for Earth System Modeling and Analysis

Irina Tezaur (ikalash@sandia.gov, Sandia National Laboratories, USA), Vincent Verjans (vincent.verjans@bsc.es, Barcelona Supercomputing Center, Spain)

Earth System Models (ESMs) integrate the interactions between atmosphere, ocean, land, ice, and biosphere to enable the simulation of the state of regional and global climate under a wide variety of conditions. Analysis using these models can enable scientists to understand potential impacts on both ecosystems and human societies.  In recent years, there has been a push to develop “next generation” ESMs, models which: (1) are able to perform realistic, high-resolution, continental scale simulations, (2) are robust, efficient and scalable on next-generation hybrid systems (multi-core, many-core, GPU) towards achieving exascale performance, (3) possess built-in advanced analysis capabilities (e.g., sensitivity analysis, optimization, uncertainty quantification), and (4) integrate machine learning capabilities to represent poorly constrained climate processes.

This minisymposium will consist of talks describing new and ongoing research in the development of accurate and tractable “next-generation” models for stand-alone ESM components (e.g., atmosphere, land-ice, sea-ice, ocean, land, biogeochemistry), as well as talks addressing the challenges in coupling ESM components for integration into global ESMs. Of particular interest are:

1. efficient computational strategies and software for tackling the complex, nonlinear, multi-scale, multi-physics problems arising in Earth system modeling, with an eye towards next-generation hybrid platforms, 
2. advanced analysis techniques that can inform/enhance existing models through the incorporation of observational data, e.g., approaches for model initialization/calibration, uncertainty quantification (UQ) and data assimilation, and
3. approaches involving the integration and application of data-driven methods, including artificial intelligence (AI) and machine learning (ML), into Earth system modeling and analysis.

Additionally, we encourage submissions on the emerging area of using ESMs and/or data to study the impacts of climate intervention/geoengineering strategies.

Computational Methods for Problems of Fluid Mechanics 

Petr Sváček (Petr.Svacek@fs.cvut.cz, Czech Technical University in Prague, Czech Republic), Jan Valášek (Jan.Valasek@fs.cvut.cz, Czech Technical University in Prague, Czech Republic)

This minisymposium focuses on modern numerical methods suitable for approximation problems in fluid mechanics, including multiphysical problems and biomechanics. We welcome contributions regarding theoretical numerical analysis, practical numerical approximation, and implementation. Possible topics include the development of accurate high-order methods as well as numerical simulations of complex flow problems. The session expects to cover a variety of computational approaches, including the Finite Volume Method (FVM), Finite Element Method (FEM), Discontinuous Galerkin (DG) methods, and others such as particle or mesh-free methods. Additionally, contributions related to turbulence modeling are highly encouraged, covering RANS, LES, or DNS approaches. Furthermore, the session will address complex coupled problems, specifically Fluid-Structure Interactions (FSI).

Recent Advances in Mathematical and Stochastic Modeling and Application

Agnieszka Wylomanska (agnieszka.wylomanska@pwr.edu.pl, Wroclaw University of Science and Technology, Poland)

This minisymposium highlights recent advances in mathematical and stochastic modeling, together with a broad range of contemporary applications. The presented methodologies span computational, probabilistic, and analytical approaches, addressing challenges arising in engineering and industrial systems as well as in weather- and environment-related extremes. A central theme of the minisymposium is the interdisciplinary nature of modern stochastic modeling, where mathematical theory, statistical inference, and domain-specific knowledge are integrated to address complex real-world problems. The talks emphasize rigorous mathematical and stochastic foundations, coupled with the analysis of real-world data, illustrating how modern theoretical developments translate into practical insight, improved understanding, and predictive capability across diverse scientific domains.