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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.
AI+Science: Accelerating Discovery is an interdisciplinary conference bringing together researchers across physics, mathematics, chemistry, biology, neuroscience, and more to examine how AI is reshaping scientific discovery.
AI+Science: Accelerating Discovery is an interdisciplinary conference bringing together researchers across physics, mathematics, chemistry, biology, neuroscience, and more to examine how AI is reshaping scientific discovery.
While Large Language Models (LLMs) show promise in many domains, relying on them for direct policy generation in games often results in illegal moves and poor strategic play.
While Large Language Models (LLMs) show promise in many domains, relying on them for direct policy generation in games often results in illegal moves and poor strategic play.
The AI Index, currently in its ninth year, tracks, collates, distills, and visualizes data relating to artificial intelligence.
The AI Index, currently in its ninth year, tracks, collates, distills, and visualizes data relating to artificial intelligence.
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.
