NVIDIA Modulus vs. Python vs. SwiftComp 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.

SwiftComp is a revolutionary multiscale, multiphysics composite simulation code that quickly and easily delivers the accuracy of 3D FEA at the efficiency of simple engineering models. In doing so, SwiftComp reduces barriers for engineers by enabling them to model composites as easily as metals using conventional structural elements in their FEA codes (without losing accuracy while capturing all the microstructural details). SwiftComp provides unified modeling for 1D (beams), 2D (plates/shells), or 3D structures, calculating all the effective properties. Use SwiftComp either independently for virtual testing of composites or as a plug-in to power your conventional structural tools with high-fidelity composites modeling. SwiftComp can compute the best structural model for use in macroscopic structural analysis, as well as perform dehomogenization to compute the pointwise stresses in the microstructure. SwiftComp directly interfaces with ABAQUS, ANSYS.