NVIDIA Modulus

NVIDIA Modulus blends physics, as expressed by governing partial differential equations (PDEs), boundary conditions, and training data to build high-fidelity, parameterized, surrogate deep learning models. The platform abstracts the complexity of setting up a scalable training pipeline, so you can leverage your domain expertise to map problems to an AI model’s training and develop better neural network architectures. Available reference application serve as a great starting point for applying the same principles to new use cases.Whether you’re a researcher looking to develop novel AI-based approaches for reimagining engineering and scientific simulations or you’re an engineer looking to accelerate design optimization and digital twin applications, the Modulus platform can support your model development. Modulus offers a variety of approaches for training physics-based neural network models, from purely physics-driven models with physics-informed neural networks (PINNs) to physics-based, data-driven architectures such as neural operators.

Documentation Center
02/03/23
NVIDIA Modulus is a deep learning framework that blends the power of physics and partial differential equations (PDEs) with AI to build more robust models for better analysis.
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High-fidelity simulations in science and engineering are computationally expensive and time-prohibitive for quick iterative use cases, from design analysis to optimization. NVIDIA Modulus, the physics machine learning platform, turbocharges such use cases by building physics-based deep learning models that are 100,000x faster than traditional methods and offer high-fidelity simulation results.
Whether you’re looking to get started with AI-driven physics simulations or working on complex nonlinear physics problems, come engage us on how the Modulus Physics-ML Model Framework can help you solve your forward, inverse, or data assimilation problems.
GitLab Supporting Modulus
NVIDIA Modulus is a deep learning framework that blends the power of physics and partial differential equations (PDEs) with AI to build more robust models for better analysis.
Utilizing Modulus techniques to solve problems ranging from developing physics-informed machine learning to modeling multi-physics simulation systems.