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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 session is specifically designed for full-time graduate students within one year of obtaining their PhD, as well as current postdoctoral scholars, fellows, and researchers.
This session is specifically designed for full-time graduate students within one year of obtaining their PhD, as well as current postdoctoral scholars, fellows, and researchers.
Save the Date. Artificial intelligence is transforming how researchers collect, analyze, and learn from data. As AI systems become increasingly integrated into scientific discovery, business decision-making, and policy analysis, they are reshaping both the questions researchers can ask and the methods they use to answer them.
Save the Date. Artificial intelligence is transforming how researchers collect, analyze, and learn from data. As AI systems become increasingly integrated into scientific discovery, business decision-making, and policy analysis, they are reshaping both the questions researchers can ask and the methods they use to answer them.
The rapid acceleration of AI comes with a profound wave of anxiety. Across every sector of society, people are facing unsettling questions about their worth and their place in a shifting world.
The rapid acceleration of AI comes with a profound wave of anxiety. Across every sector of society, people are facing unsettling questions about their worth and their place in a shifting world.
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.
