Get the latest news, advances in research, policy work, and education program updates from HAI in your inbox weekly.
Sign Up For Latest News
Constitutive modeling and parameter identification are the cornerstones of realistic biomedical simulations. For decades, the gold standard in biomedical modeling has been to select a model and then fit its parameters to data. However, the scientific criteria for model selection remain poorly understood, and the success of this approach depends largely on user experience and personal preference.
This seminar pairs a case study of AI diffusion with an organizational approach to technological changes in the workplace.
This seminar pairs a case study of AI diffusion with an organizational approach to technological changes in the workplace.
Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
In this seminar, Kuhl proposes a new method that simultaneously and autonomously discovers the model, parameters, and experiment that best explain a wide variety of biological systems toward more realistic human simulations. She illustrates how we solve this problem by formulating it as a neural network, and leverage the success, robustness, and stability of state-of-the-art optimization tools from deep learning. Out of thousands of possible models, this network discovers a unique constitutive model that outperforms traditional models and, at the same time, identifies the best experiment to train itself. These new constitutive neural networks could initiate a paradigm shift in constitutive modeling—from user-defined model selection to automated model discovery— and forever change how we simulate biomedical systems.
