Simulations are prevalent in science and engineering fields and have been recently advanced by physics-driven AI.
Join this webinar to learn how NVIDIA SimNet addresses a wide range of use cases involving coupled forward simulations without any training data, as well as inverse and data assimilation problems.
SimNet is integrated with parameterized constructive solid geometry as well as STL modules to generate point clouds and researchers can customize it with APIs to implement new geometry and physics. It also has advanced network architectures that are optimized for high-performance GPU computing and offers scalable performance for multi-GPU and multi-node implementations with accelerated linear algebra.
By attending this webinar, you’ll learn about:
- Neural network solver methodology and the SimNet architecture
- Real-world use cases, from challenging forward multi-physics simulations with turbulence and complex 3D geometries to industrial design optimization and inverse problems
- User implementation of two-phase flow in a porous media in SimNet
- SimNet results and what’s next for the toolkit