Rudina’s AI Atlas
#1: Physics-Informed Neural Networks (PINNs)
Rudina Seseri
🧠I am kicking off the AI Atlas, insights on a technology or concept that is tangibly valuable, specific, and actionable to those interested in AI, with a consequential emerging technology allowing developers to create physically-consistent and scientifically-sound AI models, significantly improving their flexibility and applicability, Physics-Informed Neural Networks (PINNs).
🤔 What are Physics-Informed Neural Networks (PINNs)?
PINNs are models where known physics equations are integrated into a neural network’s learning process, dramatically boosting the AI’s ability to produce accurate results.
Purely-digital models do not necessarily obey the fundamental laws of physical systems and thus struggle to abstract when something new happens. Conversely, the prior-knowledge constraints of PINNs restrict models to real-world behavior, yielding more interpretable ML methods that remain robust even with incomplete data.
🤔 Why Physics-Informed AI Matters and Its Limitations
Digital-only AI solutions cannot generalize well beyond their training data, limiting their adaptability. For example, a traditional artificial neural network modeling and predicting the movement of a spring.
In contrast, the above represents a physics-informed neural network solving the same problem. Its better ability to abstract leads to greater consistency, reduced training time, and better generalization far away from the training data.
The principal obstacle facing physics-informed AI is that it is inherently complicated. Creating and solving problems based on complex physical systems requires deep mathematical domain knowledge and a good deal of computational resources.
🛠Use Cases of Physics-Informed AI
The potential for PINNs to break the black box of many traditional AI models is a huge opportunity in use cases that are complex and difficult to model, or in those that require precision and accuracy for compliance. Areas that have been slow to adopt AI for these reasons include:
Manufacturing: Physics-constrained models can be used to build significantly more reliable digital twins and improve simulation. Basetwo AI is applying physics-informed AI to the biopharma, chemical, and food & beverage manufacturing spaces.
Health Sciences and Medicine: The human body is a mess of diverse interacting systems, and it’s historically been extremely difficult to model accurately. Physics-informed AI can be used to “fill in the gaps” and create stronger representations.
Energy: Google’s DeepMind has already demonstrated that such models be used in fusion science through learned plasma control to model the complexity of a reaction.
The Environment and Climate Change: using PINNs to create digital twins allows for a more holistic understanding of the complex interactions that make up the environment.
My sincere thanks to Vlad, Tyler, Thouheed, and Kyle for their valuable input!