208 citations · 246 across the 5 of their papers we have counts for
5 papers
Adaptive Block Floating-Point for Analog Deep Learning Hardware
Ayon Basumallik, Darius Bunandar, Nicholas Dronen +6
Analog mixed-signal (AMS) devices promise faster, more energy-efficient deep neural network (DNN) inference than their digital counterparts. However, recent studies show that DNNs…
Latent Space Simulation for Carbon Capture Design Optimization
Brian Bartoldson, Rui Wang, Yucheng Fu +5
The CO2 capture efficiency in solvent-based carbon capture systems (CCSs) critically depends on the gas-solvent interfacial area (IA), making maximization of IA a foundational chal…
Efficient nonlinear manifold reduced order model
Youngkyu Kim, Youngsoo Choi, David Widemann +1
Traditional linear subspace reduced order models (LS-ROMs) are able to accelerate physical simulations, in which the intrinsic solution space falls into a subspace with a small dim…
A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder
Youngkyu Kim, Youngsoo Choi, David Widemann +1
Traditional linear subspace reduced order models (LS-ROMs) are able to accelerate physical simulations, in which the intrinsic solution space falls into a subspace with a small dim…
Frames for subspaces of
Matthew Hirn, David Widemann
We present a theory of finite frames for subspaces of . The definition of a subspace frame is given and results analogous to those from frame theory for $\mathbb{C}^…