[News] SimNet @ GTC '21

SimNet On-Demand Technical Sessions from GTC '21

  • Physics-Informed Neural Networks for Mechanics of Heterogenous Media” – IIT-Bombay presented a session on Physics-Informed Neural Networks for Mechanics of Heterogeneous Media. The PINN-based NVIDIA SimNet toolkit is used to develop a framework for the simulation of damage in elastic and elastoplastic materials. For verification, SimNet results are found in good agreement with the analytical solution based on Haghighat et al, 2020.

  • Using Physics-Informed Neural Networks and SimNet to Accelerate Product Development” – Kinetic Vision presented a session on using Physics Informed Neural Networks and SimNet to accelerate product development where the Coanda effect, encountered in aerospace and several industrial applications, is simulated using SimNet. Both 2D and 3D geometries are constructed using SimNet’s internal Geometry module and simulated using modified Fourier Network Architecture. The results showed that qualitatively, the velocity flow field predicted by the commercial CFD code, Ansys Fluent and the trained SimNet PINN are very similar. Furthermore, Kinetic Vision did parametric simulations with SimNet and went a step further by taking these results and integrating them into CAD with SolidWorks for automated inference as well as providing a way for users to interact with SimNet from within Solidworks UI.

  • Hybrid Physics-Informed Neural Networks for Digital Twin in Prognosis and Health Management” – University of Central Florida presented a session on Hybrid Physics-Informed Neural Networks for Digital Twin in Prognosis and Health Management where a Digital twin model is built to predict damage and fatigue crack growth in aircraft window panels. SimNet models are based in physics and this ensures accuracy needed for prognosis and health management of structural materials. Once SimNet models are trained, they can be used to perform fast and accurate computations as a function of different input conditions. SimNet also achieves good accuracy that the commercial solvers achieve with high degree of mesh refinement. With SimNet, they can scale the predictive model to a fleet of 500 aircraft and get predictions in less than 10 seconds as opposed to taking a few days to weeks if they were to perform the same computations using high-fidelity finite element models.

  • Physics-Informed Neural Network for Flow and Transport in Porous Media” – Stanford University presented a session on Physics-Informed Deep Learning for Flow and Transport in Porous Media where a methodology is used to simulate a 2-phase immiscible transport problem (Buckley-Leverett). The model can produce an accurate physical solution both in terms of shock and rarefaction and honors the governing partial differential equation along with initial and boundary conditions. Read more about this on our NVIDIA blog here.

  • AI-Accelerated Computational Science and Engineering Using Physics-Based Neural Networks” – NVIDIA presented a session on AI-Accelerated Computational Science and Engineering Using Physics-Based Neural Networks that covers state-of-the-art AI for addressing diverse areas of applications ranging from real-time simulation (e.g., digital twin and autonomous machines) to design space exploration (generative design and product design optimization), inverse problems (e.g., medical imaging, full wave inversion in oil and gas exploration) and improved science (e.g., micromechanics, turbulence) that are difficult to solve because of various gradients and discontinuities, due to physics laws and complex shapes.