OrthoTruss™
OrthoTruss™
The materials-research workflow in miniature: sweep a reference solver to build a dataset, then train a small neural network in-browser to approximate it. Watch the surrogate approach the reference surface while its remaining error stays visible.
No install · No signup · Runs in your browser. Educational and research tool — not a medical device.
See how a fast ML emulator is built on top of a slow physics engine — the modern R&D loop.
Run a design-of-experiments sweep over the engine to generate a training set.
A small neural network trains live — watch the loss fall epoch by epoch.
See the surrogate surface approach the reference-solver surface as R² climbs toward 1.
Once trained, the surrogate predicts in milliseconds where the physics took seconds.
The build-a-surrogate loop, made visible.
Choose the parameter ranges and sample the physics engine to build a dataset.
Fit a small neural network to the data, watching the training loss fall.
Overlay the surrogate against the reference solver and read R² as it converges.
The surrogate is trained on and scored against the app’s benchmarked reference solver. Convergence measures how well the approximation reproduces that solver, not biological ground truth or clinical validity.
For teaching the ML-emulator workflow with a transparent reference solver.
A transparent teaching sandbox: the reference solver, training data, and fit metrics remain visible, while predictions stay model-dependent.
Build real intuition before the clinic — change a parameter and watch the mechanics respond, instead of memorizing rules.
Translate material and design parameters into the force system delivered — a virtual bench before you cut metal.
A surrogate (or emulator) is a fast machine-learning model trained to mimic a slow, expensive simulation. Once trained, it predicts results in milliseconds — useful for optimization and design sweeps. The sandbox builds one live.
Yes — the neural network trains in your browser, so you can watch the loss fall and the fit converge without any server.
It's at no cost, with no signup, and runs entirely in your browser — nothing is downloaded or installed.
No. It is an educational and research tool for training machine-learning surrogate models on synthetic, user-generated data. It does not use patient data and is not intended for the diagnosis or treatment planning of individual patients.
No cost, in your browser, right now.
Launch the Surrogate Sandbox →