NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation framework

Abstract

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.

References in corpus (20)

• Adam: A Method for Stochastic Optimization
• DeepXDE: A deep learning library for solving differential equations
• Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
• DGM: A deep learning algorithm for solving partial differential equations
• NSFnets (Navier-Stokes Flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations
• Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
• Adaptive activation functions accelerate convergence in deep and physics-informed neural networks
• Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data
• hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decomposition
• Searching for Activation Functions
• NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
• Weak Adversarial Networks for High-dimensional Partial Differential Equations
• SciANN: A Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks
• Implicit Neural Representations with Periodic Activation Functions
• Understanding and mitigating gradient pathologies in physics-informed neural networks
• On the Spectral Bias of Neural Networks
• When and why PINNs fail to train: A neural tangent kernel perspective
• A deep learning framework for solution and discovery in solid mechanics
• Lat-Net: Compressing Lattice Boltzmann Flow Simulations using Deep Neural Networks
• Accelerating Physics-Informed Neural Network Training with Prior Dictionaries

Cited by in corpus (15)

• Efficient training of physics-informed neural networks via importance sampling
• Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems
• Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training
• Characterizing possible failure modes in physics-informed neural networks
• Magnetostatics and micromagnetics with physics informed neural networks
• Physics-informed neural networks (PINNs) for fluid mechanics: A review
• Conditional physics informed neural networks
• Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks
• IDRLnet: A Physics-Informed Neural Network Library
• Training multi-objective/multi-task collocation physics-informed neural network with student/teachers transfer learnings
• Solving Partial Differential Equations with Point Source Based on Physics-Informed Neural Networks
• TensorDiffEq: Scalable Multi-GPU Forward and Inverse Solvers for Physics Informed Neural Networks
• One-Shot Transfer Learning of Physics-Informed Neural Networks
• A composable autoencoder-based iterative algorithm for accelerating numerical simulations
• Unsupervised Reservoir Computing for Solving Ordinary Differential Equations