NeuroForge

Turning NeuroForge into an actual engineering system

When I first started NeuroForge, it was mostly a polished frontend concept for an AI engineering platform. It looked cool, but the optimization was basically a demo.

I’ve been changing that.

The current version actually solves a structural optimization problem. A natural-language engineering brief gets converted into a structured specification, a baseline truss is sized, and NeuroForge runs real finite-element analyses on thousands of candidate designs.

The engine now includes:

2D truss FEA using the direct stiffness method
Cholesky factorization
stress, deflection, self-weight, and Euler buckling checks
constrained evolutionary optimization
simulated annealing and random search
reproducible seeded experiments
experiment history and generation-by-generation results

I also started adding a machine-learning layer. NeuroForge can generate datasets from simulation runs and train surrogate models to approximate the expensive simulation. I added polynomial ridge regression, an MLP, and Gaussian-process regression, along with held-out evaluation using MAE, RMSE, R², and prediction intervals.

The interesting part was that the results were not uniformly great. The surrogate learned mass extremely well, but predicting constraint feasibility was much harder. That gave me an actual research problem to work on instead of just another feature to add.

I also added constrained Bayesian optimization and NSGA-II for multi-objective optimization, so NeuroForge can now explore tradeoffs like mass versus compliance and display the resulting Pareto frontier.

The whole thing runs in the browser with the engineering engine separated from the React UI and heavy computation moved into a Web Worker.

Next I’m working on per-member surrogate models and uncertainty-aware candidate screening, which should make the ML system much better at predicting whether a design will actually satisfy the engineering constraints.