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

AI surrogate models have emerged as powerful tools for accelerating engineering design cycles. They provide fast, data-driven approximations of high-fidelity simulations and enable engineers to explore large design spaces more efficiently.

Teams leaning into AI physics are already accelerating R&D, cutting costs, and gaining a competitive edge by transforming how they make decisions. For example, did you catch SLB (Schlumberger) at NVIDIA GTC? Their demo offered a glimpse into the future: 100X faster results, real-time inference, and up to $1B in potential annual value through more efficient resolution of complex, high-cost engineering challenges.

While some engineers are still hesitating, the real risk is falling behind. The good news is that if you’re familiar with the mathematics behind Navier-Stokes, Maxwell, and other complex physics equations, along with the computational algorithms used to solve them, mastering deep learning will be a natural next step.

In this blog, we’ll explore what AI surrogate models are, how to get started with them, and their history, helping you understand how they can accelerate breakthroughs in your work.

What is an AI Surrogate Model?

An AI surrogate model is a mathematical approximation of a high-fidelity simulation, typically built using regression methods or neural networks. 

Regression models use statistical methods to estimate relationships between variables based on historical data. Neural networks are a type of deep learning (DL) algorithm that adjust the strength of connections between data inputs and outputs during training to minimize prediction errors. Both techniques effectively learn complex, nonlinear relationships and generate predictions with probabilistic confidence intervals.

AI surrogates are commonly implemented in Python, and can be deployed as applications that accept user input, process it through the machine learning model, and return predictions. This process, called inference, can provide real-time approximations of computationally expensive simulations.

AI surrogate models are poised to revolutionize traditional Design of Experiment (DOE) and Monte Carlo methods. Full-fidelity CAE simulations are often too time consuming and costly, forcing scientists and engineers to limit either the number of simulations or the complexity of the physics, which limits the scope of their design optimization.

In contrast, AI surrogates are trained on a smaller set of full-fidelity data and are then used to rapidly estimate results across a wider range of design variations and complex physics scenarios. This approach enables faster, more comprehensive exploration of design spaces that were once too computationally expensive to fully investigate.

Because of this, AI surrogates will drive major industry breakthroughs, as optimization leads to more innovative solutions and significantly faster time-to-market.

How Accurate are AI Surrogate Models?

AI surrogate models provide clear error metrics to identify potential issues like training data bias, model variance, and uncertainty, helping you fine tune for better accuracy and build confidence in the model’s predictions.

In deep learning, error calculations like Root Mean Squared Error (RMSE) help identify when a model’s predictions are inaccurate or when it overfits or underfits the data. For example, if RMSE is high or accuracy is below 95%, you may need to add more training data, tune the model’s hyperparameters, or improve feature engineering to reduce bias and variance.

Data plays a crucial role in the accuracy of AI surrogate models, as high-quality, diverse datasets lead to more reliable predictions. It’s important to continuously evaluate and update your data pipelines to ensure that accurate, relevant data is fed into the model, and to periodically retrain it to maintain its reliability over time.

By understanding these error measures, you can improve on your deep learning based models to achieve results that closely match full-fidelity simulations, delivering a fast, reliable solution for a wide range of applications.

The Evolution of AI Surrogate Models in Engineering

Machine learning has been integrated into mathematics, engineering, and computer science curricula since the 2010s, marking a significant shift in how we approach problem solving.

If you’re exploring machine learning mid-career, don’t worry. These concepts build on familiar statistical and optimization techniques, and there are plenty of resources to help you understand and apply them effectively. 

Here’s how we got here, mathematically speaking:

1950-1960 – Early Statistical Learning

• Linear regression was the go-to method for predicting continuous values in simulations, like temperature or stress distribution. However, this technique has limitations when dealing with complex, nonlinear behavior and cannot easily account for interactions between multiple physical variables.

1970-1980 – Advancements in Machine Learning

• Classification algorithms such as Decision Trees and Nearest Neighbors (k-NN) grew in popularity due to their ability to predict outcomes based on labeled past data. However, both methods struggle with overfitting, making it difficult for them to generalize to new data in some instances.

1990 – Support Vector Machines (SVMs) Emerge

• SVMs emerged as a powerful classification tool, improving upon decision tree-based algorithms by effectively handling complex, high-dimensional data, making them particularly useful in physics and other fields with nonlinear relationships.

2000 – The Introduction of Neural Networks (NN)

• Neural networks quickly gained traction for modeling complex, nonlinear relationships by using multiple layers to learn hierarchical features from labeled or unlabeled data, making them ideal for predicting outcomes in applications such as fluid dynamics.

2010 – The Rise of Deep Learning

• The 2010s marked the rise of deep learning, with advancements in computational methods like Convolutional Neural Networks (CNNs) and Reinforcement Learning. These innovations leveraged large datasets and high performance computing to solve previously intractable problems, accelerating progress in aerospace, automotive, and materials science.

2020 – AI-Driven Breakthroughs in Physics

• Bringing us to modern times, Physics-Informed Neural Networks (PINNs) have emerged as a transformative AI approach for solving complex physics-based problems by embedding physical laws directly into neural network training. This breakthrough has unlocked new levels of accuracy and efficiency, making it possible to tackle previously intractable challenges in engineering with unprecedented speed and reliability.

Ways to Get Started

Diving into Machine Learning can seem overwhelming, but focusing on small wins is the right approach to build confidence and momentum.

Here are three practical steps to get started:

Get Hands-on with Python

Python is the go-to language for machine learning in engineering. Getting comfortable using libraries like NumPy, Pandas, PyVista, and Matplotlib will help you to manipulate and visualize simulation data. A good first goal is to perform exploratory statistical analysis—using Python to read, summarize, and interpret CAE datasets. For instance, calculating average temperature distribution in thermal simulations or analyzing stress and strain variance in FEA can reveal critical insights before applying machine learning models.

Jupyter Notebooks are a free and user-friendly integrated development environment (IDE) that makes programming in python and data analysis more accessible. Ideal for building and testing machine learning models, they allow users to easily explore data, identify trends, and experiment with code in an interactive, shareable format.

Try Low-Code AI Tools for Quick Prototyping

For those who prefer visual, drag-and-drop workflows, low-code AI tools provide a fast and intuitive way to prototype surrogate models by automating data preprocessing, feature selection, hyperparameter tuning, and model training. The best solutions come with pre-built templates and automation scripts, abstracting complexity to make advanced machine learning more accessible while eliminating the need for extensive coding.

But ease of use isn’t just about smart AI features. If a platform can’t efficiently pull in, organize, and integrate data from multiple sources, you won’t get the full value AI can offer.

Great AI platforms can build models where data is captured and stored, leveraging centralized data to create efficient, reliable computational pipelines. By streamlining everything from data ingestion to deployment, low-code tools make prototyping AI surrogate models faster and easier.

Explore AI Focused Resources

If you’re working with large datasets, TensorFlow and PyTorch are great choices because of their scalability, flexibility, and extensive ecosystem of pre-built models. These libraries run even faster on NVIDIA GPUs, helping you train models more efficiently by leveraging accelerated cloud computing. For physics-based simulations, NVIDIA PhysicsNeMo offers pre-optimized reference architectures and real-world examples on GitHub. All of these resources help engineers augment their high-fidelity simulations with efficient AI-based models, allowing them to scale up and tackle larger, more complex problems in real-time.

The Evolution of Surrogate Models
Machine learning has been integrated into mathematics, engineering, and computer science curricula since the 2010s, marking a significant shift in how we approach problem solving.

If you’re exploring machine learning mid-career, don’t worry. These concepts build on familiar statistical and optimization techniques, and there are plenty of resources to help you understand and apply them effectively.

Conclusion

AI surrogate models are invaluable tools in modern engineering, helping reduce simulation runtimes and explore bigger design spaces. From a mathematical perspective, they are straightforward and no more complex than the engineering principles you already apply in your work.

To get started, check out Rescale’s available AI resources and consider exploring professional development courses on AI, such as those offered by MIT or Stanford. These courses can deepen your expertise and accelerate your progress in mastering AI-driven solutions.

Taking the time to learn more about AI surrogate models will both enhance your skills and reignite your passion for engineering.