For decades, progress in engineering, from aerospace to materials science, has been driven by Computer-Aided Engineering (CAE). Traditional numerical solvers are foundational, but their high computational cost often requires days or weeks for a single high-fidelity simulation, creating a significant bottleneck that limits the pace of innovation. GPU acceleration has provided a first wave of significant speedups, and now AI has the potential to further accelerate scientific discovery.
To enable this, we are advancing a new paradigm: AI-Aided Engineering (AIE), our vision for how AI research will accelerate and transform the field of Industrial Engineering, and build on NVIDIA's existing works on AI and CAE.
The core principle of AIE is to develop and train novel foundational models on observational or simulation data to learn the underlying physical laws of a system. These AI models function as highly scalable and efficient surrogates, capable of approximating complex simulations orders of magnitude faster than traditional methods—for example, FourCastNet 3 delivers 60-day weather forecasts in under 4 minutes, a 60x speedup over traditional methods.
Our Research Focus: Next-Generation Algorithms
Our specific research mission is to develop the next-generation of algorithms that will power this ecosystem. A central pillar of our work is a shift away from discretized meshes, focusing instead on models that can operate directly on the native CAD used by engineers. To achieve this, we are developing foundational algorithms, such as novel architectures for Neural Operators, that are designed not just to be fast, but to maintain a high degree of physical accuracy, and efficiently encoding geometry, as well as boundary and initial conditions.
An Open, Collaborative Approach
The AI revolution was built on open-source software and science, and a core principle of our work is that it must enable this progress and be fully open-source. By making these state-of-the-art tools accessible to everyone, we aim to democratize advanced simulation, empower the scientific community, and accelerate the next generation of AI-Aided Engineering. This creates a circular pipeline: our work is anchored in real-world industry needs, and our validated breakthroughs flow back into the entire ecosystem.
The Full-Stack Approach to AIE
At NVIDIA, we believe solving this grand challenge requires a full-stack approach, from silicon to software to systems. Our work is built upon the entire NVIDIA accelerated computing infrastructure:
- Accelerated Hardware
- The massively parallel architecture of NVIDIA GPUs provides the powerful and energy-efficient computational foundation required to train and deploy these large-scale AI models.
- Core Software
- We build upon a rich ecosystem of software, including CUDA and specialized libraries, to optimize every stage of the AI pipeline.
- Cutting Edge Research
- Our AI Aided Engineering research initiative focuses on developing the next generation of foundation AI models to enable real-time design exploration, inverse design, and digital twins across engineering domains.
- Domain specific AI models
- Once matured, trusted, benchmarked, accurate and scalable AI model architectures are made available, for instance through our Apollo models, on Hugging Face or as NIMs, NVIDIA Inference Microservices to deploy pretrained foundation model checkpoints.
- Product quality AI Physics Frameworks
- Platforms like NVIDIA Physics-Nemo provide a framework for developing physics-informed neural networks, offering robust and tested tools to train and validate these AI models.
- Datasets
- Industry scale datasets across engineering domains for training, validating and benchmarking different AI methodologies.
- Real-Time Visualization
- The ultimate goal is to connect these AI simulators to collaborative virtual worlds. NVIDIA Omniverse provides the platform to create interactive, industrial digital twins where engineers can visualize and interact with AI-driven simulations in real time.
The Future: Interactive Digital Twins
The impact of this full-stack approach is a dramatic acceleration of the design-to-analysis cycle. By providing near-instantaneous feedback through interactive digital twins within an Omniverse environment, AIE will enable engineers to explore a vastly larger design space, test more hypotheses, and arrive at optimal solutions more quickly.
We are at the beginning of an exciting new chapter in computational science. We invite the research and engineering communities to join the conversation as we explore the full potential of AI-Aided Engineering.
If you're passionate about AI, physics, and building the future of simulation, and interested in helping us push the boundaries of AI-Aided Engineering, consider applying and reach out!