What Are AI Surrogate Models? A History and How to Get Started.

AI surrogate models have become pivotal in speeding up engineering design cycles by offering rapid, data-driven approximations of high-fidelity simulations, allowing engineers to efficiently explore large design spaces. These models, typically built using regression methods or neural networks, provide real-time inference and are transforming research and development by cutting costs and enhancing decision-making processes. By integrating machine learning techniques into engineering, AI surrogates are revolutionizing traditional methods such as Design of Experiment and Monte Carlo simulations, enabling more comprehensive design exploration that was previously limited by computational expenses. The evolution of AI surrogate models has been marked by significant advancements, from early statistical learning techniques to the rise of deep learning and the introduction of Physics-Informed Neural Networks, leading to breakthroughs in complex physics-based problems with unprecedented accuracy and efficiency. Engineers can leverage Python and low-code AI tools to prototype surrogate models quickly, utilizing platforms like TensorFlow, PyTorch, and NVIDIA PhysicsNeMo to augment high-fidelity simulations, facilitating the tackling of larger, more complex problems in real-time. By understanding and applying these models, engineers can significantly enhance their capabilities, drive innovation, and maintain a competitive edge in the industry.