High Performance Computing for Dynamical Systems and Bayesian Estimation with On-Demand Computing Resources

High Performance Computing for Dynamical Systems and Bayesian Estimation with On-Demand Computing Resources Cognitive modeling often requires high computational resources. Complex models must often be run for long periods of time, and experiments must often be repeated. Many researchers limit their models and designs to meet these computational restrictions, however using the right tools, models can be distributed and scaled to meet the computational requirements of the most complex models. In this workshop, we aim to show a flexible and scalable modeling environment that can be used in both local settings, and High Performance Computing (HPC) Clusters. We will present and make use of the Julia programming language, some of its tools for modeling dynamical systems, and Bayesian parameter estimation, as well as the methods to offload workloads from your desktop to HPC clusters.

Recommended citation: Barradas-Chacón, L. A. (2025). High Performance Computing for Dynamical Systems and Bayesian Estimation with On-Demand Computing Resources. Workshop for the International Conference on Mathematical Psychology and Cognitive Modeling (MathPsych/ICCM), 2025, Ohio, USA.
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