no code implementations • 25 Apr 2024 • Yongxu Jin, Dalton Omens, Zhenglin Geng, Joseph Teran, Abishek Kumar, Kenji Tashiro, Ronald Fedkiw
Since loose-fitting clothing contains dynamic modes that have proven to be difficult to predict via neural networks, we first illustrate how to coarsely approximate these modes with a real-time numerical algorithm specifically designed to mimic the most important ballistic features of a classical numerical simulation.
no code implementations • 5 May 2023 • Daniel Johnson, Trevor Maxfield, Yongxu Jin, Ronald Fedkiw
Various software efforts embrace the idea that object oriented programming enables a convenient implementation of the chain rule, facilitating so-called automatic differentiation via backpropagation.
no code implementations • 25 Jan 2022 • Yongxu Jin, Yushan Han, Zhenglin Geng, Joseph Teran, Ronald Fedkiw
We present a novel paradigm for modeling certain types of dynamic simulation in real-time with the aid of neural networks.
no code implementations • 20 Jan 2020 • Jane Wu, Yongxu Jin, Zhenglin Geng, Hui Zhou, Ronald Fedkiw
Regularization is used to avoid overfitting when training a neural network; unfortunately, this reduces the attainable level of detail hindering the ability to capture high-frequency information present in the training data.
