13 citations · 16 across the 7 of their papers we have counts for
7 papers
Predicting Wave Reflection and Transmission in Heterogeneous Media via Fourier Operator-Based Transformer Modeling
Zhe Bai, Hans Johansen
We develop a machine learning (ML) surrogate model to approximate solutions to Maxwell's equations in one dimension, focusing on scenarios involving a material interface that refle…
StFT: Spatio-temporal Fourier Transformer for Long-term Dynamics Prediction
Da Long, Shandian Zhe, Samuel Williams +2
Simulating the long-term dynamics of multi-scale and multi-physics systems poses a significant challenge in understanding complex phenomena across science and engineering. The comp…
FTL: Transfer Learning Nonlinear Plasma Dynamic Transitions in Low Dimensional Embeddings via Deep Neural Networks
Zhe Bai, Xishuo Wei, William Tang +3
Deep learning algorithms provide a new paradigm to study high-dimensional dynamical behaviors, such as those in fusion plasma systems. Development of novel model reduction methods,…
AutoCT: Automated CT registration, segmentation, and quantification
Zhe Bai, Abdelilah Essiari, Talita Perciano +1
The processing and analysis of computed tomography (CT) imaging is important for both basic scientific development and clinical applications. In AutoCT, we provide a comprehensive…
Non-intrusive Nonlinear Model Reduction via Machine Learning Approximations to Low-dimensional Operators
Zhe Bai, Liqian Peng
Although projection-based reduced-order models (ROMs) for parameterized nonlinear dynamical systems have demonstrated exciting results across a range of applications, their broad a…
Randomized methods to characterize large-scale vortical flow network
Zhe Bai, N. Benjamin Erichson, Muralikrishnan Gopalakrishnan Meena +2
We demonstrate the effective use of randomized methods for linear algebra to perform network-based analysis of complex vortical flows. Network theoretic approaches can reveal the c…
Dynamic mode decomposition for compressive system identification
Zhe Bai, Eurika Kaiser, Joshua L. Proctor +2
Dynamic mode decomposition has emerged as a leading technique to identify spatiotemporal coherent structures from high-dimensional data, benefiting from a strong connection to nonl…