Run Analysis
Stage 1: Geometry → Stage 2: Sections → Stage 3: Materials & Analysis
🔑 Agent LLM Settings
Use defaults (DEEPSEEK_MODEL)Pipeline Progress
0/6 agents ready2. Sections & Material Properties
Probabilistic characterization of material properties · constitutive model details
Run the pipeline to see cross-section geometry.
🔬 Material Uncertainty
Run the pipeline to extract material uncertainty information from your prompt.
Uncertainty Parameters
No material uncertainty data yet.
Constitutive Model Details
Constitutive model information will appear here after pipeline execution.
3. MC Simulation
Downstream: parallel sampling of material uncertainty (after geometry & sections are defined)
Simulation Control
MC settings derived from the pipeline prompt. Run the pipeline first.
Sampling Distribution
0 / 0 OKHistogram + theoretical PDF curve + MC sample points (rug plot). Blue = theory, purple = histogram, red = KDE, green = mean.
Drift Distribution
Inter-story drift ratio histogram per story. Dashed red = mean, dotted gold = μ±σ. CSV saved in simulation case folder.
Response Scatter
Max displacement vs sampled parameter. Red dashed = linear trend.
1. Geometry — Structural Visualization
Detailed node/element inspection, geometry validation & consistency checks
🖱 Hover over elements to see section dimensions and material properties. Click to flag issues.
Run the pipeline to see automatic geometry validation results.
🔍 Error Feedback
0 item(s) selectedClick nodes/elements in the diagrams above to select them, then classify each error type below.
No items selected. Click a node or element in the visualization to begin.
Benchmark Batch Runner
Run randomized test cases and review results
Prompt Run History
Recent submitted prompts and their outcomes
RL Optimization & Feedback
Reward signals and human feedback collection
Reward Score
Reward Components
Human Feedback
Mark the current resultMark the current result as correct or incorrect. This feedback is incorporated into RL reward computation.
Prompt Lifecycle
ProductionRun stages in order: Optimization → Confirmation → Production. Production uses only promoted frozen prompts.
Loading candidate prompts...
🧬 MIPROv2 Prompt Optimizer
IdleGenerates candidate prompts from recorded successes and failures during Optimization. Candidates must still pass Confirmation before they can be promoted to Production.
Run optimization to see results here. Each round uses the LLM to analyze failure cases and propose improved prompts.
Code Execution
Run generated code and view section force diagrams
Stdout
Stderr
Complete Code
Generated OpenSeesPy codePython Check Agent Diagnosis
Section Force Diagrams
IdleRun generated code to render diagrams.
Agent Rewards
Per-agent RL reward decomposition and experience summary
Loading agent reward data...