180 citations · 244 across the 16 of their papers we have counts for
7 papers · 1 filter
Perturbative methods for mostly monotonic probabilistic satisfiability problems
Stephen Eubank, Madhurima Nath, Yihui Ren +1
The probabilistic satisfiability of a logical expression is a fundamental concept known as the partition function in statistical physics and field theory, an evaluation of a relate…
Smart sensors using artificial intelligence for on-detector electronics and ASICs
Gabriella Carini, Grzegorz Deptuch, Jennet Dickinson +19
Cutting edge detectors push sensing technology by further improving spatial and temporal resolution, increasing detector area and volume, and generally reducing backgrounds and noi…
Solving Simulation Systematics in and with AI/ML
Brett Viren, Jin Huang, Yi Huang +5
Training an AI/ML system on simulated data while using that system to infer on data from real detectors introduces a systematic error which is difficult to estimate and in many ana…
DUNE Software and High Performance Computing
Bonnie Fleming, Kyle Knoepfel, Meifeng Lin +6
DUNE, like other HEP experiments, faces a challenge related to matching execution patterns of our production simulation and data processing software to the limitations imposed by m…
UVCGAN: UNet Vision Transformer cycle-consistent GAN for unpaired image-to-image translation
Dmitrii Torbunov, Yi Huang, Haiwang Yu +5
Unpaired image-to-image translation has broad applications in art, design, and scientific simulations. One early breakthrough was CycleGAN that emphasizes one-to-one mappings betwe…
A Deep Finite Difference Emulator for the Fast Simulation of Coupled Viscous Burgers' Equation
Xihaier Luo, Yihui Ren, Wei Xu +3
This work proposes a deep learning-based emulator for the efficient computation of the coupled viscous Burgers' equation with random initial conditions. In a departure from traditi…
A Bayesian Deep Learning Approach to Near-Term Climate Prediction
Xihaier Luo, Balasubramanya T. Nadiga, Yihui Ren +3
Since model bias and associated initialization shock are serious shortcomings that reduce prediction skills in state-of-the-art decadal climate prediction efforts, we pursue a comp…