From load case to verdict,
inside one dashboard.
Parametric Study —
automate your design space.
Define a parameter range, set the number of steps, and let SimBond sweep your connection through our full FEM pipeline automatically. No manual reruns.
Sweep any parameter
Vary geometry, mesh density, solver tolerances, or load magnitude across a configurable range — any combination, any number of steps.
Full FEM per iteration
Each iteration runs the full build → mesh → solve → post chain on our engine. Results stream back as jobs complete.
Batch result table
Every run lands in a sortable results table — stress, displacement, convergence — ready to compare and export.
Physics you can audit,
precision that learns.
SimBond bonds a proven physics baseline with machine learning — combining the trust of an explicit formula with the accuracy of 560 full FEM simulations.
Physics foundation
An improved SRSS formula with coupling coefficients establishes a transparent, code-aligned resistance baseline — no black box.
ML residual correction
A physics-informed neural network (PINN), trained on 560+ in-house FEM cases, learns the residual the physics misses, sharpening every prediction.
Instant verdict
Pass / fail classification with a resistance band and safety margin — returned in under three seconds, in your browser.
Structural facadesTruss connections
Glass curtain wallsFive-axis loads in.
A verdict out.
Dial in N, Vy, Vz, My and Mz and watch the hybrid model resolve resistance in real time — the same engine that powers the full design tool.
The accessible alternative
to desktop FEM.
Legacy connection tools are powerful but heavy — desktop-bound, per-seat, and opaque. SimBond keeps the rigor and drops the friction.
| Capability | SimBond | Legacy FEM suites |
|---|---|---|
| Engine | ✓ Our cloud FEM engine | Proprietary desktop solvers |
| Deployment | ✓ Cloud — runs in any browser | Desktop install |
| Licensing | ✓ Pay-as-you-go tokens | Per-seat annual license |
| Result speed | ✓ < 3 seconds (AI surrogate) | Minutes (full FEM) |
| Method | ✓ Physics-informed neural networks (PINNs) | Black-box internals |
Every prediction is validated against 560+ of our own full FEM simulations. See the methodology →
