  We present SimNet, an AI-driven multi-physics simulation framework, to
accelerate simulations across a wide range of disciplines in science and
engineering. Compared to traditional numerical solvers, SimNet addresses a wide
range of use cases - coupled forward simulations without any training data,
inverse and data assimilation problems. SimNet offers fast turnaround time by
enabling parameterized system representation that solves for multiple
configurations simultaneously, as opposed to the traditional solvers that solve
for one configuration at a time. SimNet is integrated with parameterized
constructive solid geometry as well as STL modules to generate point clouds.
Furthermore, it is customizable with APIs that enable user extensions to
geometry, physics and network architecture. It has advanced network
architectures that are optimized for high-performance GPU computing, and offers
scalable performance for multi-GPU and multi-Node implementation with
accelerated linear algebra as well as FP32, FP64 and TF32 computations. In this
paper we review the neural network solver methodology, the SimNet architecture,
and the various features that are needed for effective solution of the PDEs. We
present real-world use cases that range from challenging forward multi-physics
simulations with turbulence and complex 3D geometries, to industrial design
optimization and inverse problems that are not addressed efficiently by the
traditional solvers. Extensive comparisons of SimNet results with open source
and commercial solvers show good correlation.
