1. [Home](https://developer.nvidia.com) 2. [Topics](https://developer.nvidia.com/topics) 3. Computer Aided Engineering # Computer-Aided Engineering (CAE) Explore how NVIDIA is enabling CAE industry developers to accelerate physics-based CAE simulations and embrace real-time interactive design using AI-accelerated digital twins. Get Started With Interactive Digital Twins [Explore API Catalog](https://developer.nvidia.com) Key Topics: - CAE Simulation - Real-Time Digital Twins - AI Emulation [![](https://developer.download.nvidia.com/images/cae/what-is-cae.jpg)](https://developer.download.nvidia.com/images/cae/what-is-cae.jpg) _Click Image to Enlarge_ ## What is CAE? Computer-aided engineering (CAE) encompasses a diverse range of numerical approaches, such as [computational fluid dynamics](https://www.nvidia.com/en-us/use-cases/computational-fluid-dynamics-simulation/), finite element analysis, and electromagnetic analysis, with the primary goal of helping engineers and scientists research and develop products like cars, planes, and consumer goods. At their heart, these approaches typically solve some form of partial differential equations (PDEs) through direct or iterative linear solvers. NVIDIA CUDA-X™ libraries provide CAE developers with the core building blocks to accelerate their CAE applications. See how AI-powered CAE workflows combine high-fidelity simulation data with surrogate models to deliver real-time, interactive analysis in this [technical blog](/blog/how-to-run-ai-powered-cae-simulations/). * * * ## Explore CUDA-X Libraries That Accelerate CAE ### NVIDIA PhysicsNeMo PhysicsNeMo is an open-source physics AI framework to empower CAE developers to build, train, and fine-tune AI surrogate models using the latest machine learning (ML) architectures. [Learn More](https://developer.nvidia.com/physicsnemo) ### NVIDIA Warp NVIDIA Warp is a developer framework for building and accelerating data generation and spatial computing in Python. Warp gives coders an easy way to write kernel-based programs for CAE and machine learning. Warp supports PyTorch, JAX, PhysicsNeMo, and NVIDIA Omniverse™ libraries. [Learn More](https://developer.nvidia.com/blog/creating-differentiable-graphics-and-physics-simulation-in-python-with-nvidia-warp) ### NVIDIA CUDA-X Libraries NVIDIA CUDA-X , built on CUDA®, is a collection of libraries that deliver dramatically higher performance across compute-intensive application domains, including AI and CAE for common tasks, such as solving sparse and dense linear algebra. [Learn More](https://developer.nvidia.com/gpu-accelerated-libraries) ### NVIDIA cuBLAS NVIDIA cuBLAS is an accelerated computing library for AI and scientific computing applications. cuBLAS includes API extensions for providing drop-in industry standard BLAS APIs and GEMM APIs with support for fusions that are highly optimized for NVIDIA architectures. [Learn More](https://developer.nvidia.com/cublas) ### NVIDIA cuSOLVER The NVIDIA cuSOLVER library provides a wide collection of decompositions and linear system solvers that deliver significant acceleration for many CAE-relevant algorithms. [Learn More](https://developer.nvidia.com/cusolver) ### NVIDIA cuSPARSE NVIDIA cuSPARSE provides high-performance APIs with GPU-accelerated basic linear algebra routines for AI and HPC applications. cuSPARSELt host APIs provide structured sparsity support and no-call overhead with JIT LTO. [Learn More](https://developer.nvidia.com/cuSPARSE) ### NVIDIA cuFFT NVIDIA cuFFT is a library that provides GPU-accelerated Fast Fourier Transform (FFT) implementations that can be used within CAE for applications like spectral methods, deep learning, advanced flow-field post-processing, and aero-acoustics analysis. [Learn More](https://developer.nvidia.com/cufft) ### NVIDIA cuDSS NVIDIA cuDSS is a library of GPU-accelerated linear solvers for sparse matrices. cuDSS offers performant reordering, factorization, and solve capabilities and supports a variety of matrix types and numerics. With single-GPU, multi-GPU, and multi-node support, cuDSS supports a wide range of CAE use cases. [Learn More](https://developer.nvidia.com/cudss-downloads) ### AmgX AmgX is a fully GPU-accelerated core solver library that speeds up the often computationally intense linear solver portion of CAE simulations. The library is well suited for implicit unstructured simulations, providing popular Krylov methods and preconditioners, including algebraic multigrid. [Learn More](https://developer.nvidia.com/amgx) ### NVIDIA Omniverse NVIDIA Omniverse is a collection of libraries and microservices for developing industrial digital twins and physical AI simulation applications. [Learn More](https://developer.nvidia.com/omniverse) * * * ## Explore CAE Industries ![Automotive](https://developer.download.nvidia.com/icons/m48-vehicle-transportation-front.svg) ### Automotive ![Energy](https://developer.download.nvidia.com/icons/m48-energy-industry.svg) ### Energy ![Industrial and Manufacturing](https://developer.download.nvidia.com/icons/m48-robot-manufacturing.svg) ### Industrial and Manufacturing ![Super Computing](https://developer.download.nvidia.com/icons/m48-hpc-supercomputing.svg) ### Super Computing ![Higher Education and Research](https://developer.download.nvidia.com/icons/m48-stackedbooks.svg) ### Higher Education and Research ![Interactive Computer-Aided Engineering (CAE) simulations for automotive designs](https://developer.download.nvidia.com/images/cae/use-ai-to-accelerate-cae-solutions.jpg) ## Use AI to Accelerate CAE Solutions for Enterprise Transform design and simulation processes with NVIDIA’s full-stack innovation across accelerated infrastructure, enterprise-grade software, and AI models. AI-accelerated digital twins enable real-time interactive design, allowing engineers to instantly see the impact on key performance indicators. This accelerates the design workflow, delivering faster production, higher accuracy, efficiency, and infrastructure performance at a lower overall cost. [Learn More](https://www.nvidia.com/en-us/solutions/cae/)