Simulation Intelligence Platform

Stop wasting 70% of your simulation budget

Forge tells you what to simulate next, when to stop, and where your model is trustworthy. Without replacing ANSYS. Without building an ML team.

Run Your Last Study Through Forge → See Reduction Proof
92%
Data Reduction
R² 0.997
Surrogate Accuracy
<30s
Training Time
Simulation Reduction Proof

Real CFD Data. Real Results.

These results are from a real patient-specific aortic blood flow CFD simulation. Forge converged at 3,000 training points with R²=0.997.

Study Parameters

DatasetCardiovascularPINNs — Aortic Blood Flow
SolverSimVascular (7M elements)
Mesh64,304 nodes × 370,486 cells
FieldsPressure, Velocity (3D), Speed
SurrogateRandom Forest (100 trees)
Training time0.8 seconds (CPU)

Forge Results

3,000
Training Points Needed
92%
Data Reduction
0.997
R² at Convergence
Approved
Trust Status
Active Learning Curve Parity Plot
Error Distribution Per-Field Accuracy
The Problem

Your solver is not the problem. Your workflow is.

ANSYS, Simcenter, and OpenFOAM solve physics correctly. But they give zero guidance on what to simulate next, when to stop, or whether 200 runs are enough.

Without Forge

Brute-force simulation campaigns

  • Run 300–800 simulations “just to be safe”
  • $500K–$2M per program in compute + engineer time
  • Weeks of wall-clock time waiting for solver queues
  • No way to know when you have “enough” data
  • Over-simulation. Delayed decisions. No confidence.
With Forge

Intelligent simulation campaigns

  • Run 60–150 targeted simulations with proven coverage
  • Quantified STOP: R²=0.95, coverage 94%, plateau detected
  • Surrogate fills gaps between runs in milliseconds
  • Trust score on every prediction — green/yellow/red
  • 60–80% reduction. Weeks faster. Provable confidence.
How It Works

Four Steps to Confident Decisions

You keep your solver, your mesh, your process. Forge adds the intelligence layer.

1

Upload Results

Drop VTU, CSV, or Parquet from any solver. Forge auto-detects inputs, outputs, and data type.

2

Train Surrogate

Forge picks the right algorithm for your data. Trains in seconds. No ML expertise needed.

3

See What’s Missing

Trust maps show exactly where the model is confident and where it needs more data.

4

Decide or Refine

Accuracy sufficient? Stop. Not enough? Forge exports the next batch to run.

Works with your existing tools

ANSYS FluentANSYS MechanicalSTAR-CCM+SimcenterOpenFOAMCOMSOLAbaqusLS-DYNAEDEMSimVascularMoldflow
Decision Confidence Layer

Every Prediction is Scored. No Black Boxes.

Forge will NOT let you trust a bad model. If the model is not credible, Forge blocks ROI claims and shows a red banner.

📊

Accuracy Gate

R² threshold enforcement. Below 0.70 = red banner.

🔬

Ensemble Vote

3 models vote. Disagreement = don’t trust this region.

🚧

OOD Detection

Outside training boundaries? Flagged immediately.

⚙️

Physics Limits

Your constraints checked on every prediction.

🗺️

Region Trust Map

Green = reliable. Red = run more simulations.

Trust is earned, not assumed. If the model is not credible, Forge blocks ROI claims. A red “Model Not Credible” banner replaces green metrics.

Objection Handled

Why This Isn’t Built Into Your Solver

Simulation tools optimize physics. Forge optimizes decision efficiency.

Your Simulation ToolForge
Core jobSolve physics equationsDecide which simulations to run
Stopping criteriaNone — you decide “enough”Explicit STOP with 4 evidence signals
Trust scoringNone — results are results5-layer trust verification
Next stepNone“Run these 8 simulations next”
Engineering Domains

Any Physics Your Solver Can Compute

Solver-agnostic. Physics-agnostic. If it produces result files, Forge ingests it.

🌊

CFD & Flow

Pressure, velocity, drag, HTC. Internal/external flow, mixing, blood flow.

🔩

Structural FEA

Stress, safety factor, fatigue, displacement. Brackets, implants, housings.

🌡️

Thermal

Temperature, resistance, heat flux. Electronics cooling, heat exchangers.

💊

Pharma & DEM

Dissolution, mixing, coating, drying. Particle simulations, process development.

Business Impact

Enterprise Impact, Per Program

Typical CFD Program
$300K–$1.2M

Cost Saved

300–800 brute-force runs reduced to 60–150 targeted runs. Same coverage, proven accuracy.

Schedule Impact
4–6 weeks

Faster Decisions

Surrogate predictions in milliseconds. Iterate 1000× between simulation runs.

Pharma / Medtech
14 fewer

Experiments Per Study

Each experiment: $5K–$50K. Forge identifies exactly which ones you can skip.

ROI computed per campaign from actual training data. Forge only claims savings when the model meets accuracy thresholds.

Send Us Your Last Simulation Dataset

We’ll show you which runs were wasted, where your model is unreliable, and how many runs you actually needed.

Get Simulation Reduction Proof → Back to SigmaTwin