20 citations · 48 across the 11 of their papers we have counts for
4 papers · 1 filter
Fully Convolutional Spatiotemporal Learning for Microstructure Evolution Prediction
Michael Trimboli, Mohammed Alsubaie, Sirani M. Perera +2
Understanding and predicting microstructure evolution is fundamental to materials science, as it governs the resulting properties and performance of materials. Traditional simulati…
A Low-complexity Structured Neural Network Approach to Intelligently Realize Wideband Multi-beam Beamformers
Hansaka Aluvihare, Sivakumar Sivasankar, Xianqi Li +2
True-time-delay (TTD) beamformers can produce wideband, squint-free beams in both analog and digital signal domains, unlike frequency-dependent FFT beams. Our previous work showed…
A Low-complexity Structured Neural Network to Realize States of Dynamical Systems
Hansaka Aluvihare, Levi Lingsch, Xianqi Li +1
Data-driven learning is rapidly evolving and places a new perspective on realizing state-space dynamical systems. However, dynamical systems derived from nonlinear ordinary differe…
Beyond Regular Grids: Fourier-Based Neural Operators on Arbitrary Domains
Levi Lingsch, Mike Y. Michelis, Emmanuel de Bezenac +3
The computational efficiency of many neural operators, widely used for learning solutions of PDEs, relies on the fast Fourier transform (FFT) for performing spectral computations.…