Stanford Webinar - Democratizing Model Discovery with Neural Networks

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Professor Ellen Kuhl's Background and Research

Professor Ellen Kuhl is a professor at Stanford's School of Engineering and focuses on improving human health through computational simulation.
She is the founder of the Living Matter Lab, which uses physics-based tools embedded in machine learning to model living systems, such as the heart, arteries, lungs, muscles, and the brain.
The lab collaborates with medical professionals and device designers to help develop therapies and understand the impact of forces on the heart.

In the past, researchers would select a model from a library of existing models that best fit their data points.
This required expertise in mechanics and physics, limiting its use to experts in the field.
The process of selecting a model was manual and time-consuming, relying on the researcher's knowledge and experience.

The Living Matter Lab has developed a method to automate the model discovery process using neural networks.
Neural networks are trained to select the best model based on input data, such as mechanical tests of tissues.
The neural network output includes model parameters that represent stiffness, shear modulus, and other physical properties.
The automated process removes the need for manual selection and allows non-experts to use these tools safely.

Constitutive neural networks are a type of neural network that incorporates physics and engineering principles into the model discovery process.
Physics parameters and constraints, such as symmetry and elastic behavior, are hardwired into the network.
The resulting models are more interpretable, with parameters that have physical meanings.

The automated model discovery process has applications beyond medical research, such as modeling the mechanical properties of artificial meat.
In healthcare, it can be used to model the human brain and better understand conditions like Alzheimer's disease and traumatic brain injuries.
It can aid in the design of medical devices, choosing the safest regions to perform surgery, and optimizing therapies for individual patients.
The Living Matter Lab's code and resources for this technology are open-source and available on GitHub.
The lab offers a graduate-level course in automated model discovery for professionals interested in learning more about the methods and applications.