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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 workshop will cover how NVIDIA RAPIDS offers a seamless experience to enable GPU-acceleration for many existing data science tasks with zero code changes. You will learn how to use GPU-accelerated tools to conduct data science faster, leading to more scalable, reliable, and cost-effective results!
This workshop will cover how NVIDIA RAPIDS offers a seamless experience to enable GPU-acceleration for many existing data science tasks with zero code changes. You will learn how to use GPU-accelerated tools to conduct data science faster, leading to more scalable, reliable, and cost-effective results!
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.
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.
