Do it all collaboratively, in the cloud or locally, with the power of Python and JAX
Collimator makes it simple to optimize your controls. No matter your strategy—Auto-tuning, local optimization, stochastic perturbation, and more—you’ll have an optimized controller in no time thanks to automatic differentiation.
Are you trying to develop a control algorithm for a system with challenging dynamics? Maybe the physics aren’t well-understood? Using tools like SINDy, Collimator makes identifying plant models fast, and as precise as you require.
Whether you’re doing Physics-Informed Machine Learning, using a Digital Twin, or simply training a Neural Network, Collimator’s ML tools have it covered. We have flexible data pipelines, auto-diff, and parameterized models to suit your needs.
Parameter sweeps? Sure. Monte Carlo simulation? No problem. Parallel distributed simulations and GPU acceleration? You bet. Collimator has the compute resources you need, and the tooling to seamlessly take full advantage of them.
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