NVIDIA Modulus vs. Python vs. Simulink Comparison Chart

NVIDIA Modulus is a neural network framework that blends the power of physics in the form of governing partial differential equations (PDEs) with data to build high-fidelity, parameterized surrogate models with near-real-time latency. Whether you’re looking to get started with AI-driven physics problems or designing digital twin models for complex non-linear, multi-physics systems, NVIDIA Modulus can support your work. Offers building blocks for developing physics machine learning surrogate models that combine both physics and data. The framework is generalizable to different domains and use cases—from engineering simulations to life sciences and from forward simulations to inverse/data assimilation problems. Provides parameterized system representation that solves for multiple scenarios in near real time, letting you train once offline to infer in real time repeatedly.

Design and simulate your system in Simulink before moving to hardware. Explore and implement designs that you wouldn’t otherwise consider – without having to write C, C++, or HDL code. Explore a wide design space by modeling the system under test and the physical plant. Your entire team can use one multi-domain environment to simulate how all parts of the system behave. Package and share your simulations with collaborators, suppliers, and clients. Reduce expensive prototypes by testing your system under conditions that are otherwise too risky or time-consuming to consider. Validate your design with hardware-in-the-loop testing and rapid prototyping. Maintain traceability from requirements to design to code. Instead of writing thousands of lines of code by hand, automatically generate production-quality C and HDL code that behaves the same way as the model you created in Simulink. Then deploy it directly onto your embedded processor or FPGA/ASIC.