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Cost for the live or on demand training will be $800 (USD).
The LLMs for HEC-RAS Users module provides hands-on instruction for engineers and modelers who want to apply large language models (LLMs) and AI agents to practical HEC-RAS workflows. This module assumes students have some prior HEC-RAS experience, but does not require programming or AI experience.
The course will cover the fundamentals of how LLMs work, their capabilities and limitations, how to communicate effectively with them, and how to apply them to HEC-RAS-related tasks. Example applications will include working with HEC-RAS documentation, extracting and interpreting results data from HEC-RAS model files, generating custom outputs such as plots, tables, and GIS data, and programmatically modifying model input parameters.
The course will be divided into sessions that are approximately 1 hour long and will feature follow-along workshops that allow students to apply concepts directly to HEC-RAS use cases. Emphasis will be placed on practical workflows, validation of AI outputs, and understanding both the capabilities and limitations of large language models.
The course will culminate in an applied workshop that brings together some of the core concepts and demonstrates how AI can improve efficiency, insight, and communication in HEC-RAS workflows while keeping engineering judgment at the center. The total course length will be between 6-8 hours.
The on-demand format allows students to learn at their own pace and fully explore the modeling workshops between sessions. Live training may be offered depending on level of demand.
Dates for the course release is TBD but will open sometime in 2026.
Cost for the live or on demand training will be $800 (USD).
The LLMs for HEC-RAS Users module provides hands-on instruction for engineers and modelers who want to apply large language models (LLMs) and AI agents to practical HEC-RAS workflows. This module assumes students have some prior HEC-RAS experience, but does not require programming or AI experience.
The course will cover the fundamentals of how LLMs work, their capabilities and limitations, how to communicate effectively with them, and how to apply them to HEC-RAS-related tasks. Example applications will include working with HEC-RAS documentation, extracting and interpreting results data from HEC-RAS model files, generating custom outputs such as plots, tables, and GIS data, and programmatically modifying model input parameters.
The course will be divided into sessions that are approximately 1 hour long and will feature follow-along workshops that allow students to apply concepts directly to HEC-RAS use cases. Emphasis will be placed on practical workflows, validation of AI outputs, and understanding both the capabilities and limitations of large language models.
The course will culminate in an applied workshop that brings together some of the core concepts and demonstrates how AI can improve efficiency, insight, and communication in HEC-RAS workflows while keeping engineering judgment at the center. The total course length will be between 6-8 hours.
The on-demand format allows students to learn at their own pace and fully explore the modeling workshops between sessions. Live training may be offered depending on level of demand.
Dates for the course release is TBD but will open sometime in 2026.