AtomWorld enables the first direct atomistic simulation of RPV steel at year-and-meter scales, handling ten-quintillion-atom systems and simulating one service year in 1.71 days with 92-97% scaling efficiency on leadership supercomputers.
Mixed citations
Real-time Bayesian inference at extreme scale: A digital twin for tsunami early warning applied to the Cascadia subduction zone
Mixed citation behavior. Most common role is background (60%).
6
Pith papers citing it
Background
60% of classified citations
citation-role summary
background 3
dataset 1
method 1
citation-polarity summary
years
2026 6representative citing papers
FP64 tensor cores accelerate high-order finite-element kernels in MFEM by up to 2x with 83% energy gains and near-perfect weak scaling on exascale hardware.
A new overdensity-conditioned emulator trained on small subvolumes from Quijote recovers the global halo mass function via integration over the overdensity distribution at 0.026% of the simulation cost.
A reformulation of Bayesian OED as dense matrix subset selection plus a pipelined Schur-complement greedy algorithm on hundreds of GPUs enables optimization of 175-sensor networks for billion-degree-of-freedom tsunami models with near-perfect scaling.
A clustering-aware correction algorithm using spatial partitioning and projected gradient descent preserves single-linkage clusters in lossy-compressed particle data while keeping competitive compression ratios.
citing papers explorer
-
Unfolding an Atomistic World: Atomistic Simulation of Reactor Pressure Vessel Steel Across Year-and-Meter Scales
AtomWorld enables the first direct atomistic simulation of RPV steel at year-and-meter scales, handling ten-quintillion-atom systems and simulating one service year in 1.71 days with 92-97% scaling efficiency on leadership supercomputers.
-
Accelerating High-Order Finite Element Simulations at Extreme Scale with FP64 Tensor Cores
FP64 tensor cores accelerate high-order finite-element kernels in MFEM by up to 2x with 83% energy gains and near-perfect weak scaling on exascale hardware.
-
Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters
A new overdensity-conditioned emulator trained on small subvolumes from Quijote recovers the global halo mass function via integration over the overdensity distribution at 0.026% of the simulation cost.
-
Sensor Placement for Tsunami Early Warning via Large-Scale Bayesian Optimal Experimental Design
A reformulation of Bayesian OED as dense matrix subset selection plus a pipelined Schur-complement greedy algorithm on hundreds of GPUs enables optimization of 175-sensor networks for billion-degree-of-freedom tsunami models with near-perfect scaling.
-
Preserving Clusters in Error-Bounded Lossy Compression of Particle Data
A clustering-aware correction algorithm using spatial partitioning and projected gradient descent preserves single-linkage clusters in lossy-compressed particle data while keeping competitive compression ratios.
- cuRAMSES: Scalable AMR Optimizations for Large-Scale Cosmological Simulations