redadehy2

We're excited to share the deep dive into our overhauled modeling and simulation engine at Collimator. Our engine brings together the best of Python, JAX, Drake, and cloud technology to optimize engineering workflows, particularly in aerospace and robotics.

In the article, we cover: - The rationale behind our choices - How our engine leverages JAX for performance and automatic differentiation - The integration of machine learning with traditional physics-based modeling - Advanced optimization techniques - Real-world applications and examples

We'd love to hear your thoughts and feedback!

At Collimator.ai we built a differentiable modeling and simulation platform for dynamical systems on JAX (think Simulink but in the cloud and with Python+JAX instead of Matlab) in order to enable more efficient optimization, autotuning, MPC, neural network control, etc. Here is a simple example of PID tuning with JAX and autodiff: https://py.collimator.ai/examples/pid_tuning/ (feedback is welcome!)

Collimator | Software Engineer (multiple roles) | Full-Time | Remote Worldwide

Collimator is developing modern cloud-native Modeling and Simulation software. We just closed our $2M seed round led by S28 and are assembling our core engineering team. Founders have both technical MS degrees (EE and CS) and MBA from Stanford, with multiple startup exits and VC experience.

We are hiring front-end / back-end / embedded software developers as well as electrical / mechanical engineers who can code. If you enjoy solving challenging technical problems across multiple engineering and scientific disciplines, and building from the ground up complex systems and tools for other engineers, you should reach out!

Learn more at https://angel.co/company/collimator-ai or email reda@collimator.ai.