Workshop organized by: D.T. Hristopulos
This workshop focuses on theoretical, computational, and experimental studies that use data‑driven methods inspired by statistical physics, spatiotemporal statistics, and machine learning to analyze complex natural systems. We welcome contributions that develop or apply such tools to extract insight from large, noisy, or multiscale environmental datasets. Interdisciplinary work combining theory, data analysis, or computational experiments is especially encouraged.
Applications: Examples include environmental transport processes, natural hazards (e.g., earthquakes, fires, avalanches, landslides), extreme events (e.g., heat waves, drought, heavy precipitation), and coupled climate–hydrology processes.
Topics of Interest
- Methods for large spatiotemporal data analysis
- Nonlinear and multiscale modeling of natural systems
- Reconstruction and simulation of porous media with non‑Gaussian statistics
- Stochastic differential equations in environmental processes
- Upscaling and homogenization techniques
- Complex‑network approaches to environmental systems
- Long‑range correlations and multifractality in environmental data