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 physics-informed neural network (PINNs) 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.
Solves larger problems faster with accelerated linear algebra (XLA) and automatic mixed precision (AMP) support and multi-GPU/multi-node implementation.
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.
For more details on SimNet, please refer to NVIDIA SimNet™: an AI-accelerated multi-physics simulation framework. This paper reviews the neural network solver methodology, the SimNet architecture, and the features needed to effectively solve the PDEs. It also includes real-world use cases that range from challenging forward, parameterized, multi-physics simulations with turbulence and complex 3D geometries for industrial design optimization and real-time simulations to inverse and data assimilation problems that are not addressed efficiently by traditional solvers.
What Others Are Saying
“SimNet’s clear APIs, clean and easily navigable code, environment and hardware configurations well handled with dockers, scalability, ease of deployment and the competent support team made it easy to adopt and has provided some very promising results. This has been great so far and we look forward to using SimNet on problems with much larger dimensions.”
Cedric Frances, PhD Student, Stanford University
“SimNet is an AI based physics simulation toolkit that has the potential to unlock amazing capabilities in industrial and scientific simulation.”
Christopher Lamb, VP of Computing Software, NVIDIA
“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
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