# GSM Presentation/Verification Framework: Refined Concept

This document refines the idea of a unified framework to present, verify, and chain multiple scenarios of GSM-based material models (as described in gsm_presentation_framework.md). It focuses on a sequential yet extensible approach that can incorporate branching, persistence, and postprocessing, ultimately supporting iterative workflows such as parameter calibration, phenomenological checks, and scenario-based modeling.

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## 1. Core Objectives

1. Provide a standardized process to:
   - Define a sequence of loading scenarios (e.g., monotonic strain control, stress control, cyclic tests).
   - Run GSM model simulations for each scenario.
   - Extract key results (e.g., peak stress, critical strain, damage onset time).
   - Feed those results into subsequent scenarios.

2. Support an extensible set of “scenario classes” that encapsulate the type of loading (strain- or stress-control), time function, and relevant postprocessors (e.g., max stress detection).

3. Enable optional persistence (saving/loading intermediate states) for efficiency in complex processes or distributed experiments.

4. Allow branching logic in workflows (e.g., follow one path if damage occurs early, another path otherwise).

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## 2. Proposed Design

### 2.1 ScenarioBase
• A base class representing a generic scenario definition.  
• Defines mandatory methods:  
  – prepare_input() → Prepares the (time, control) arrays.  
  – run_simulation(model) → Calls the appropriate GSMModel methods (get_F_response or get_G_response).  
  – postprocess(response_data) → Extracts criterion values (peak stress, strain at damage, etc.).  

### 2.2 Scenario Implementations
• Subclasses for various loading modes:  
  – MonotonicStrainScenario  
  – MonotonicStressScenario  
  – CyclicStrainScenario  
  – CreepRelaxationScenario  
  – etc.  

Each scenario class can define specialized input generation (e.g., piecewise time steps) and postprocessing (e.g., identify loops or relaxation plateaus).

### 2.3 Execution Workflow
• A simple manager (e.g., ScenarioExecutor) that:  
  1. Iterates over a list of scenarios.  
  2. Instantiates each scenario, calls prepare_input, then runs run_simulation with a configured GSMModel instance.  
  3. Collects postprocessed results.  
  4. Stores results in a structured data container (e.g., a JSON-based or HDF5-based store).

### 2.4 Parameter Update & Branching
• Each scenario’s postprocess step can update or refine material parameters and feed them to the next scenario’s GSMModel creation.  
• A decision layer can branch into different scenario sequences based on measured criteria (e.g., if peak stress < threshold, skip certain tests).

### 2.5 Persistence & Data Management
• Store each scenario’s input arrays, response data, and postprocessing outcomes in an external database or file structure.  
• Provide quick reloading/resuming capabilities to avoid recomputing expensive steps.

### 2.6 Integration with gsm_lagrange
• The scenario classes rely on existing GSMModel instances.  
• The scenario design does not require changes to GSMDef or GSMEngine; they remain the symbolic/numerical core.  
• Minor additions to GSMModel or ResponseData may streamline repeated tasks (e.g., isolating the portion of JSON writing logic).

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## 3. Implementation Suggestions

1. Start with a “scenario” folder inside gsm_lagrange (or an adjacent package).  
2. Provide a base class (ScenarioBase) and a few specialized scenarios to showcase the approach (MonotonicStrainScenario, MonotonicStressScenario).  
3. Create a minimal ScenarioExecutor to manage scenario lists and parameter handoffs.  
4. Incorporate a small postprocessing library in each scenario (e.g., detect peak, measure plastic/viscous strain).  
5. If needed, integrate a known workflow engine or incremental runner (e.g., Prefect, Luigi, or smaller Python-based solutions) to manage branching more robustly.  
6. Document each scenario’s typical usage, highlighting how to combine it with your existing param dictionaries and GSMModel generation.

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## 4. Potential Extensions

• Graph-based or UML-based representation of scenario branching.  
• Automatic generation of notebooks or reports for each scenario step.  
• Parameter calibration loops (e.g., scenario → compare with experiment → parameter update → repeat).  
• Visualization modules using standardized plots (stress-strain curves, internal variable evolution, hysteresis loops).

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## 5. Conclusion

By encapsulating loading definitions, model calls, and postprocessing in discrete scenario classes, this framework allows incremental, reusable, and transparent presentation/verification of GSM material models. Adopting a simple manager for executing sequences of scenarios and enabling optional branching lays the groundwork for advanced tasks like calibration and multi-stage verification, all while maintaining separation between symbolic definitions (gsm_lagrange) and scenario-specific logic.
