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Found 4 papers, 1 papers with code
Application of probabilistic modeling and automated machine learning framework for high-dimensional stress field
Modern computational methods, involving highly sophisticated mathematical formulations, enable several tasks like modeling complex physical phenomenon, predicting key properties and design optimization.
Distilling Governing Laws and Source Input for Dynamical Systems from Videos
Distilling interpretable physical laws from videos has led to expanded interest in the computer vision community recently thanks to the advances in deep learning, but still remains a great challenge.
Uncovering Closed-form Governing Equations of Nonlinear Dynamics from Videos
Distilling analytical models from data has the potential to advance our understanding and prediction of nonlinear dynamics.
Extracting full-field subpixel structural displacements from videos via deep learning
This paper develops a deep learning framework based on convolutional neural networks (CNNs) that enable real-time extraction of full-field subpixel structural displacements from videos.
