AI-Accelerated Simulation Toolkit

Simulations are pervasive in science and engineering. They are computationally expensive and don't easily accommodate measured data coming from sources such as sensors or cameras. NVIDIA SimNet™ is a simulation toolkit, which addresses these challenges using AI and physics. Whether you're looking to get started with AI-driven physics simulations or working on complex nonlinear physics problems, NVIDIA SimNet is your toolkit for solving forward, inverse, or data assimilation problems.

Scalable Performance

Solves larger problems faster with accelerated linear algebra (XLA) and asymmetric multiprocessing (AMP) support and multi-GPU/multi-node implementation.

Broad Applicability

Models multiple physics types in forward and inverse simulations with accuracy and convergence.

Fast Turnaround Time

Provides parameterized system representation that solves for multiple scenarios simultaneously.

Easy to Adopt

Provides application programming interfaces (APIs) for implementing new physics and geometry and detailed user guide examples.

SimNet Multi-GPU/Multi-Node Performance

NVIDIA SimNet supports multi-GPU and multi-node scaling using Horovod. This allows for multiple processes, each targeting a single GPU, with collective communication using the NVIDIA Collective Communications Library (NCCL) and MPI.

This plot shows the weak scaling performance of SimNet on an FPGA test problem running on up to the 32 V100 GPUs in four DGX-1 systems. The scaling efficiency from one to 32 GPUs is more than 85%.

SimNet Weak Scaling Across Multiple GPUs


Novel Neural Network Architecture

SimNet provides a framework to model partial differential equations (PDEs) along with boundary and initial conditions. A key concept to solving the flow problems involves modeling the mass balance condition as a hard constraint as well as a global constraint. This improves accuracy as well as convergence characteristics. Additionally, for multi-physics problems spanning multiple domains, separate networks for the different physics with coupling at the domain interfaces work well.

Design Space Exploration

While traditional numerical solvers are designed to solve one configuration at a time, SimNet is able to work with multiple single geometries or parameterized geometry. The neural networks can be trained on multiple scenarios simultaneously and can evaluate each configuration in real time during inference. This allows the design space to be explored more efficiently.

Optimized for Multi-Physics Problems

SimNet is not only able to solve problems with multiple physics more efficiently with use of parameterized geometries but is also able to expand the scope of traditional simulations beyond currently solvable use cases. For example, the network can retain the knowledge gained during training and later solve the learned scenarios in real time. Similarly, the data assimilation and inverse problems that aren’t solved by the numerical solvers can be easily tackled by the neural networks.

What Others Are Saying

“We believe that SimNet has some unique features like parameterized geometries for multi-physics problems and multi-GPU/multi-node neural network implementation. We are looking forward to incorporating SimNet in our research and teaching activities.”

Professor Hadi Meidani, Civil and Environmental Engineering, University of Illinois at Urbana-Champaign

Please send feedback and comments to the NVIDIA SimNet team

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