Search Results for author:
Found 7 papers, 2 papers with code
Combined track finding with GNN & CKF
The application of Graph Neural Networks (GNN) in track reconstruction is a promising approach to cope with the challenges arising at the High-Luminosity upgrade of the Large Hadron Collider (HL-LHC).
Gauge-equivariant pooling layers for preconditioners in lattice QCD
We demonstrate that gauge-equivariant pooling and unpooling layers can perform as well as traditional restriction and prolongation layers in multigrid preconditioner models for lattice QCD.
Gauge-equivariant neural networks as preconditioners in lattice QCD
We demonstrate that a state-of-the art multi-grid preconditioner can be learned efficiently by gauge-equivariant neural networks.
Differentiated uniformization: A new method for inferring Markov chains on combinatorial state spaces including stochastic epidemic models
Both are challenging to compute when the state space and hence the size of $Q$ is huge.
Machine learning for surface prediction in ACTS
We present an ongoing R&D activity for machine-learning-assisted navigation through detectors to be used for track reconstruction.
ECM modeling and performance tuning of SpMV and Lattice QCD on A64FX
We present an architectural analysis of the A64FX used in the Fujitsu FX1000 supercomputer at a level of detail that allows for the construction of Execution-Cache-Memory (ECM) performance models for steady-state loops.
Performance Distributed, Parallel, and Cluster Computing High Energy Physics - Lattice
Scale-invariant biomarker discovery in urine and plasma metabolite fingerprints
Motivation: Metabolomics data is typically scaled to a common reference like a constant volume of body fluid, a constant creatinine level, or a constant area under the spectrum.
